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The emergence of cylindrical vector beam (CVB) multiplexing has opened new avenues for high-capacity optical communication. Although several configurations have been developed to couple/separate CVBs, the CVB multiplexer/demultiplexer remains elusive due to lack of effective off-axis polarization control technologies. Here we report a straightforward approach to realize off-axis polarization control for CVB multiplexing/demultiplexing based on a metal–dielectric–metal metasurface. We show that the left- and right-handed circularly polarized (LHCP/RHCP) components of CVBs are independently modulated via spin-to-orbit interactions by the properly designed metasurface, and then simultaneously multiplexed and demultiplexed due to the reversibility of light path and the conservation of vector mode. We also show that the proposed multiplexers/demultiplexers are broadband (from 1310 to 1625 nm) and compatible with wavelength-division-multiplexing. As a proof of concept, we successfully demonstrate a four-channel CVB multiplexing communication, combining wavelength-division-multiplexing and polarization-division-multiplexing with a transmission rate of 1.56 Tbit/s and a bit-error-rate of 10−6 at the receive power of −21.6 dBm. This study paves the way for CVB multiplexing/demultiplexing and may benefit high-capacity CVB communication.

To address the integrated scheduling problem involving no-wait constraints between processes in the actual production of complex products with both symmetric and asymmetric branches, and the need for cross-workshop equipment network collaboration and equipment flexibility, a no-wait network flexible integrated scheduling algorithm based on idle-time optimization and process rescheduling is proposed (ITPR-NFIS). Based on the concepts of non-terminal flexible process groups, terminal flexible process groups, and virtual no-wait flexible process groups, the algorithm first determines the scheduling sequence for non-terminal flexible process groups and virtual no-wait flexible process groups using the reverse layer priority strategy and the average reverse subsequent path strategy. Then, the no-wait earliest completion strategy, the optimal completion-semi-idle triggered insertion rescheduling strategy, and the optimal completion-full-idle adaptive insertion scheduling strategy are proposed to determine the processing machine and processing time for the target process. Finally, for terminal flexible process groups, the scheduling sequence is determined based on the completion time of their reverse preceding process, and the processing machine and processing time of the terminal flexible process groups are determined by the no-wait earliest completion strategy and the optimal completion-full-idle adaptive insertion scheduling strategy. The example shows that the algorithm can effectively solve the integrated scheduling problem with no-wait constraints in cross-workshop equipment networks, whether applied to symmetric, asymmetric, or mixed-structure complex products. It significantly reduces the total processing time and enhances production efficiency.

To address the issue of further collaboratively optimizing process continuity, time cost, and equipment utilization in identical two-workshop distributed integrated scheduling, an identical two-workshop distributed integrated scheduling algorithm based on the improved bipartite graph (DISA-IBG) is proposed. The method introduces an improved bipartite graph cyclic decomposition strategy that incorporates both the topological characteristics of the process tree and the dynamic resource constraints of the workshops. Based on the resulting substrings, a multi-substring weight scheduling strategy is constructed to achieve a systematic evaluation of substring priorities. Finally, a substring pre-allocation strategy is designed to simulate the scheduling process through virtual allocation, which enables dynamic adjustments to resource allocation schemes during the actual scheduling process. Experimental results demonstrate that the algorithm reduces the total product makespan to 37 h while improving the overall equipment utilization to 67.8%, thereby achieving the synchronous optimization of \"shorter processing time and higher equipment efficiency.\" This research provides a feasible scheduling framework for intelligent sensor-enabled manufacturing environments and lays the foundation for data-driven collaborative optimization in cyber-physical production systems.

In this study, we explored an optimization method for a comprehensive power dispatching system based on the fusion of ant colony algorithm and dynamic weight scheduling strategy. Firstly, the limitations of the existing scheduling system are introduced. Then, the proposed optimization methods are elaborated in detail, including the basic principle of ant colony algorithm, the design of dynamic weight scheduling strategy, and the fusion mode of the two. A large number of experimental data prove that this method is superior to the traditional scheduling method. Experimental results show that the integrated scheduling system optimization method based on ant colony algorithm and dynamic weight scheduling strategy significantly improves the scheduling efficiency and resource utilization. Specifically, the method reduces the average dispatch time by 20% and improves the resource utilization by 15% when dealing with large-scale power dispatching problems. This indicates that the method has high practical value and can provide strong support for the optimization of scheduling system in related fields.

This paper addresses the flexible job-shop scheduling problem with multiple time factors—namely, transportation time and setup time—as well as job priorities (referred to as FJSP-JPC-TST). An optimization model is established with the objective of minimizing the completion time. Considering the characteristics of the FJSP-JPC-TST, we propose an improved whale optimization algorithm that incorporates multiple strategies. First, a two-layer encoding mechanism based on operations and machines is introduced. To prevent illegal solutions, a priority-based encoding repair mechanism is designed, along with an active scheduling decoding method that fully considers multiple time factors and job priorities. Subsequently, a multi-level sub-population optimization strategy, an adaptive inertia weight, and a cross-population differential evolution strategy are implemented to enhance the optimization efficiency of the algorithm. Finally, extensive simulation experiments demonstrate that the proposed algorithm offers significant advantages and exhibits high reliability in effectively solving such scheduling problems.

The engineering of porous transport layer (PTL)–catalyst layer (CL) interfacial architecture plays a critical role in optimizing the performance of proton exchange membrane water electrolyzers (PEMWEs). Particularly, at the PTL-CL interface, our results reveal that anode catalyst loadings affect the modulation of the PTL surface structure on the overpotentials of PEMWEs. Under high anode catalyst loadings, the magnitude of overpotentials is predominantly governed by the electronic conductivity and mass transport resistance within the CL, where the modifying effects of PTL-CL interfacial contact characteristics become negligible. However, when the catalyst loading is reduced, the PTL-CL interfacial contact characteristics become critical for electron conduction, mass transport, and kinetic reaction. Under low catalyst loadings, the etched PTL demonstrates a maximum reduction of 59 mV compared to the pristine PTL at 4 A/cm2, with the former exhibiting a 10 mΩ·cm2 reduction. Meanwhile, the etched PTL integrated with a cell demonstrates superior performance in both mass transport and kinetic overpotentials compared to a pristine PTL. This clearly indicates that the surface structure of the PTL plays an increasingly significant role in regulating the overpotentials of PEMWEs as the catalyst loadings decrease.

Concrete is accelerating its transition towards green and low-carbon development, but its performance throughout its entire life cycle is significantly influenced by environmental changes, which remains a key technical challenge currently faced. The effects of early-age concrete tensile damage on thermal conductivity and moisture transport properties, as well as their coupling mechanism, remain unclear, leading to severe cracking. To explore the cracking mechanism of early-age concrete under the coupled conditions of environment and damage and to evaluate its performance throughout its lifecycle, this article conducts comparative experiments on the performance of concrete under high temperature, varying humidity, and damage conditions in the early age stage. The variation law of temperature, humidity, and strain of concrete is studied, and the evolution of microstructure and composition of concrete is explored. The response of porosity to ambient humidity exhibits opposite trends between restrained and unrestrained specimens, with rates of change of +0.0353%/RH and −0.0245%/RH, respectively. Furthermore, the study identified a critical turning point in ambient relative humidity (50% RH), which significantly alters the degree of hydration (Ca/Si ratio) of the concrete. The research results may provide theoretical and technical support for cracking risk assessment and crack control throughout the entire life cycle of concrete thin-walled structures.

Quality inspection is a crucial step in ensuring product conformity and avoiding rework waste, while job priority constraints are prevalent in the production of complex products with assembly structures. This paper presents a modeling and solution framework for the multi-objective flexible job shop scheduling problem that incorporates both quality inspection activities and job priority constraints. An optimization model is constructed with the objectives of minimizing the makespan, minimizing the total energy consumption, and maximizing the processing quality. To solve this model, an improved multi-objective evolutionary algorithm based on decomposition is developed, which integrates several well-established mechanisms into a unified framework. The algorithm integrates multi-product assembly structures via virtual nodes, employs a two-vector encoding scheme, and incorporates a product—group repair mechanism based on binary sorting tree to handle job priority constraints. To maintain diversity among non-dominated solutions, a niching-based elite archive strategy is adopted. Furthermore, a quality enhancement strategy and a memory vector-based local search mechanism are embedded to strengthen the algorithm’s search capability. Simulation results demonstrate that the proposed algorithm outperforms the compared algorithms in terms of both convergence and diversity.

Climate change and increasing urbanization have contributed greatly to urban flooding, making it a global problem. The resilient city approach provides new ideas for urban flood prevention research, and currently, enhancing urban flood resilience is an effective means for alleviating urban flooding pressure. This study proposes a method to quantify the resilience value of urban flooding based on the `4R' theory of resilience, by coupling the urban rainfall and flooding model to simulate urban flooding, and the simulation results are used for calculating index weights and assessing the spatial distribution of urban flood resilience in the study area. The results indicate that (1) the high level of flood resilience in the study area is positively correlated with the points prone to waterlogging; the more an area is prone to waterlogging, the lower the flood resilience value. (2) The flood resilience index in most areas shows a significant local spatial clustering effect, the number of areas with nonsignificant local spatial clustering accounting for 46% of the total. The urban flood resilience assessment system constructed in this study provides a reference for assessing the urban flood resilience of other cities, thus facilitating the decision-making process of urban planning and disaster mitigation.

To evaluate the effect of deep learning model on cone beam (CB) CT image analysis of patients with acute pulpitis. The improved principle of maximum entropy and minimum energy method (PME-MEM’) was proposed to preprocess CBCT images. The conditional generative adversarial network (cGAN) model of deep learning was adopted to segment images. In this study, 80 cases of acute pulpitis in our hospital were selected as the research objects. CT images of the patients were collected and pretreated with PME-MEM. The denoising effects of different Gaussian noise treatments were compared and analyzed, and cGAN model was used to segment different parts of teeth in the image. The treatment plan was made according to the processed CT images, and patients were rolled into two groups according to the treatment methods, with 40 cases in each group. The modified group received one-off root canal treatment, and the traditional group received multiple root canal treatments. The postoperative treatment effects of the patients were observed. The results showed that the PME-MEM’ had a better denoising effect on CBCT images relative to the original PME-MEM. The deep learning cGAN model can realize the segmentation of caries, enamel, dentin, dental pulp, crown, restoration, and root canal in CBCT images. The clinical treatment results showed that the treatment time and postoperative pain score of the modified group were considerably reduced versus those of the traditional group (P < 0.05). The postoperative comfort score and satisfaction with treatment results increased greatly (P < 0.05). In short, deep learning can be used to segment the target position in CBCT images of patients. Combined with one-off root canal therapy, the therapeutic effect was ideal for patients with acute pulpitis.