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Acoustic metasurfaces play a key role in building acoustics, noise control, and acoustic cloaking by regulating the acoustic wave scattering characteristics through subwavelength structures. The design of diffusely reflecting metasurfaces aims to achieve a uniform distribution of a scattered field, which is essentially a high-dimensional nonconvex optimization problem that needs to balance the computational efficiency in the synergistic optimization of the spatial arrangement of cells and the angular response. In traditional methods, a heuristic algorithm is prone to local optimization, and it is difficult to balance the global search and local adjustment. And full-wave simulation is time consuming and seriously restricts the design efficiency. Therefore, the hybrid tornado-gradient descent optimization algorithm (VDGD) is proposed in this paper. It uses a two-stage collaborative optimization approach to refine the reflection angle distribution of acoustic metasurfaces, thereby enhancing the uniformity of the diffuse acoustic field. The Tornado Optimization Algorithm (TOA) was initially employed to introduce global perturbations to the randomly initialized design. Local optimization can be avoided by gradually decreasing the perturbation magnitude, which reduces the standard deviation of the sound field from about 5.81 dB to about 4.07 dB. Then, the gradient descent is used for local fine adjustment to further reduce the standard deviation to about 1.91 dB. Experimental results show that the VDGD algorithm outperforms the seven classical and up-to-date optimization algorithms in improving scattering uniformity. This method achieves an effective balance between global search and local fine tuning, providing an efficient and flexible optimization strategy for metasurface design, which can bring application support for intelligent acoustic devices and sound field regulation.

Traditional ant colony algorithms for mobile robot path planning often suffer from slow convergence, susceptibility to local optima, and low search efficiency, limiting their applicability in dynamic and complex environments. To address these challenges, this paper proposes an improved trimming ant colony optimization (ITACO) algorithm. The method introduces a dynamic weighting factor into the state transition probability formula to balance global exploration and local exploitation, effectively avoiding local optima. Additionally, the traditional heuristic function is replaced with an artificial potential field attraction function, dynamically adjusting the potential field strength to enhance search efficiency. A path-length-dependent pheromone increment mechanism is also proposed to accelerate convergence, while a triangular pruning strategy is employed to remove redundant path nodes and shorten the optimal path length. Simulation experiments show that the ITACO algorithm improves the path length by up to 62.86% compared to the classical ACO algorithm. The ITACO algorithm improves the path length by 6.68% compared to the latest related research results. These improvements highlight the ITACO algorithm as an efficient and reliable solution for mobile robot path planning in challenging scenarios.

Three-dimensional path planning is critical for the successful operation of unmanned aerial vehicles (UAVs), automated guided vehicles (AGVs), and robots in industrial Internet of Things (IIoT) applications. In 3D path planning, the standard Particle Swarm Optimization (PSO) algorithm suffers from premature convergence and a tendency to fall into local optima, leading to significant deviations from the optimal path. This paper proposes an improved PSO (IPSO) algorithm that enhances particle diversity and randomness through the introduction of logistic chaotic mapping, while employing dynamic learning factors and nonlinear inertia weights to improve global search capability. Experimental results demonstrate that IPSO outperforms traditional methods in terms of path length and computational efficiency, showing potential for real-time path planning in complex environments.

Background Abiotic stresses have increasingly serious effects on the growth and yield of crops. Cold stress, in particular, is an increasing problem. In this study, Fragaria daltoniana and F. vesca were determined to be cold-resistant and cold-sensitive species, respectively. Integrated transcriptomics and metabolomics methods were used to analyze the regulatory mechanism of abscisic acid (ABA) in F. daltoniana and F. vesca in their response to low temperature stress. Results F. daltoniana and F. vesca increased their ABA content under low temperature stress by upregulating the expression of the ABA biosynthetic pathway gene NCED and downregulating the expression of the ABA degradative gene CYP707A . Both types of regulation increased the accumulation of glucose and fructose, resulting in a reduction of damage under low temperature stress. Twelve transcription factors were found to be involved in the ABA regulatory pathway. The strong cold tolerance of F. daltoniana could be owing to its higher levels of ABA that accumulated compared with those in F. vesca under low temperature stress. In addition, the gene ABF2 , which is related to the transduction of glucose signaling, was significantly upregulated in the leaves of F. daltoniana , while it was downregulated in the leaves of F. vesca under low temperature stress. This could contribute to the higher levels of glucose signal transduction in F. daltoniana . Thus, this could explain the higher peroxidase activity and lower damage to cell membranes in the leaves of F. daltoniana compared with F. vesca under low temperature stress, which endows the former with stronger cold tolerance. Conclusions Under low temperature stress, the differences in the accumulation of ABA and the expression trends of ABF2 and ABF4 in different species of wild strawberries may be the primary reason for their differences in cold tolerance. Our results provide an important empirical reference and technical support for breeding resistant cultivated strawberry plants.

The Water Production and Supply (WP&S) industry faces the dual objectives of ensuring water supply security and achieving carbon neutrality. An integrated analytical framework (Measurement-Assessment-Identification) was developed to measure carbon dioxide emissions from the WP&S industry (WP&S-CO 2 emissions), assess the CO 2 emissions reduction potential of the WP&S industry (WP&S-CRP), and identify the key driving forces of WP&S-CRP. In the measurement module, a method for measuring CO 2 emissions specific to the WP&S industry is proposed. In the assessment module, we clarify the concept of CO 2 emissions reduction potential within the WP&S industry and develop a corresponding assessment model. In the identification module, a multiple model for identifying the driving factors of WP&S-CRP is constructed. This framework is applied to a long-term case study across various provinces and regions in China. The results indicate that: (1) Since 2010, China’s WP&S-CO 2 emissions have experienced fluctuating growth, with a spatial distribution pattern that decreases from coastal to inland areas. The WP&S industry in the North coast and Guangdong Province showed the highest emissions on two spatial scales, respectively. (2) Against the backdrop of an increasing total water supply, the WP&S-CRP of China decreased from 0.75 in 2010 to 0.38 in 2022, indicating a gradual improvement in green production levels within the industry. However, regions such as the Northeast, Middle Yellow River, and provinces like Hebei and Heilongjiang still exhibit high levels of WP&S-CRP. (3) The water for production and operation and the pipe network density are the most significant positive and negative driving forces for WP&S-CRP, respectively. This study offers potential references for developing sustainable development strategies for the WP&S industry in various provinces of China with the goal of carbon neutrality.

Accurate and real-time quality prediction to realize the optimal process control at a competitive price is an important issue in Industrial 4.0. This paper shows a successful engineering application of how smart soft sensors can be combined with machine learning technique to significantly save human resources and improve performance under complex industrial conditions. Ensemble learning based soft sensors succeed in capturing complex nonlinearities, frequent dynamic changes, as well as time-varying characteristics in industrial processes. However, local model regions under traditional ensemble modelling methods are highly dependent on labeled data samples and, hence, their prediction accuracy might get affected when labeled samples are limited. A novel active learning (AL) framework upon the ensemble Gaussian process regression (GPR) model is proposed for smart soft sensor design in order to overcome this drawback. Firstly, to iteratively select the most informative unlabeled samples for labeling with hierarchical sampling based AL strategy, to then apply Gaussian mixture model (GMM) technique to autonomously identify operation phases, to further construct local GPR models without human involvement, and finally to integrate the base predictors by applying the Bayesian fusion strategy. Comparative studies for the penicillin fermentation process demonstrate the reliability and superiority of the recommended smart soft sensing. The cost of human annotation can be dramatically reduced by at least half while the prediction performance simultaneously keeps high.

Accurate prediction of student graduation status is crucial for higher education institutions to implement timely interventions and improve student success. While existing methods often rely on single data sources or generic model architectures, this paper proposes a novel Multi-Branch Convolutional Neural Network (MBCNN) that systematically integrates multi-dimensional factors influencing student outcomes. The model employs eight dedicated branches to capture both subjective and objective features from four key dimensions: student characteristics, school resources, family environment, and societal factors. Through robust normalization and hierarchical feature fusion, MBCNN effectively learns discriminative representations from these heterogeneous data sources. Evaluated on a real-world dataset from Polytechnic Institute of Portalegre, our approach demonstrates superior performance compared to traditional and up-to-date machine learning methods, achieving improvements of 4.07–17.35% in accuracy, 4.60–20.19% in weighted precision, 4.07–17.35% in weighted recall, and 4.59–18.73% in weighted F1-score. The results validate that domain-specific neural architectures, designed to align with the inherent structure of educational data, significantly enhance prediction accuracy and generalization capability.

Zero-shot stance detection aims to identify the stance expressed in social media text aimed at specific targets without relying on annotated data. However, due to insufficient contextual information and the inherent ambiguity of language, this task faces numerous challenges in low-resource scenarios. This work proposes a novel zero-shot stance detection method based on multi-agent debate (ZSMD) to address the aforementioned challenges. Specifically, we construct two debater agents representing the supporting and opposing stances. A knowledge enhancement module supplements the original tweet and target with relevant background knowledge, providing richer contextual support for argument generation. Subsequently, the two agents engage in debate over a predetermined number of rounds, employing rebuttal strategies such as factual verification, logical analysis, and sentiment analysis. If no consensus is reached within the specified rounds, a referee agent synthesizes the debate process and original input information to deliver the final stance determination. We evaluate ZSMD on two benchmark datasets, SemEval-2016 Task 6 and P-Stance, and compare it against strong zero-shot baselines such as MB-Cal and COLA. The experimental results show that ZSMD not only achieves higher accuracy than these baselines, but also provides deeper insights into subtle differences in opinion expression, highlighting the potential of structured argumentation in low-resource settings.

The Dynamic Multi-objective Optimization Problem (DMOP) is one of the common problem types in academia and industry. The Dynamic Multi-Objective Evolutionary Algorithm (DMOEA) is an effective way for solving DMOPs. Despite the existence of many research works proposing a variety of DMOEAs, the demand for efficient solutions to DMOPs in drastically changing scenarios is still not well met. To this end, this paper is oriented towards DMOEA and innovatively proposes to explore the correlation between different points of the optimal frontier (PF) to improve the accuracy of predicting new PFs for new environments, which is the first attempt, to our best knowledge. Specifically, when the DMOP environment changes, this paper first constructs a spatio-temporal correlation model between various key points of the PF based on the linear regression algorithm; then, based on the constructed model, predicts a new location for each key point in the new environment; subsequently, constructs a sub-population by introducing the Gaussian noise into the predicted location to improve the generalization ability; and then, utilizes the idea of NSGA-II-B to construct another sub-population to further improve the population diversity; finally, combining the previous two sub-populations, re-initializing a new population to adapt to the new environment through a random replacement strategy. The proposed method was evaluated by experiments on the CEC 2018 test suite, and the experimental results show that the proposed method can obtain the optimal MIGD value on six DMOPs and the optimal MHVD value on five DMOPs, compared with six recent research results.

In this paper, the parameter estimation of bilinear state-space systems with missing outputs is studied. The bilinear model is transformed into a linear time-varying state-space model, and Kalman smoother with a time-varying gain is adopted to estimate missing outputs and unmeasurable states. Under the expectation-maximization (EM) algorithm scheme, an iterative estimation algorithm based on Kalman smoother is derived, in which the unknown parameters, missing outputs, and unmeasurable states can be estimated simultaneously. Two simulation examples, including a numerical example and a three-tank system experiment, are adopted to verify the effectiveness of the proposed algorithm.