This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem (DHHBFSP) designed to minimize the total tardiness and total energy consumption simultaneously, and proposes an improved proximal policy optimization (IPPO) method to make real-time decisions for the DHHBFSP. A multi-objective Markov decision process is modeled for the DHHBFSP, where the reward function is represented by a vector with dynamic weights instead of the common objective-related scalar value. A factory agent (FA) is formulated for each factory to select unscheduled jobs and is trained by the proposed IPPO to improve the decision quality. Multiple FAs work asynchronously to allocate jobs that arrive randomly at the shop. A two-stage training strategy is introduced in the IPPO, which learns from both single- and dual-policy data for better data utilization. The proposed IPPO is tested on randomly generated instances and compared with variants of the basic proximal policy optimization (PPO), dispatch rules, multi-objective metaheuristics, and multi-agent reinforcement learning methods. Extensive experimental results suggest that the proposed strategies offer significant improvements to the basic PPO, and the proposed IPPO outperforms the state-of-the-art scheduling methods in both convergence and solution quality.
The photochemical conversion of plastic waste into valuable resources under ambient conditions is challenging. Achieving efficient photocatalytic conversion necessitates intimate contact between the photocatalyst and plastic substrate, as water molecules are readily oxidized by photogenerated holes, potentially bypassing the plastic as the electron donor. This study demonstrated a novel strategy for depositing polystyrene (PS) waste onto a photoanode by leveraging its solubility in specific organic solvents, including acetone and chloroform, thus enhancing the interface contact. We used an anodization technique to fabricate a skeleton-like porous tungsten oxide (WO3) structure, which exhibited higher durability against detachment from a conductive substrate than the WO3 photoanode fabricated using the doctor blade method. Upon illumination, the photogenerated holes were transferred from WO3 to PS, promoting the oxidative degradation of plastic waste under ambient conditions. Consequently, the oxidative degradation of PS on the anode side generated carbon dioxide, while the cathodic process produced hydrogen gas through water reduction. Our findings pave the way for sunlight-driven plastic waste treatment technologies that concurrently generate valuable fuels or chemicals and offer the dual benefits of cost savings and environmental protection.
Enhancing ecological security for sustainable social, economic, and environmental development is a key focus of current research and a practical necessity for ecological management. However, the integration of retrospective ecological security assessments with future trend predictions and fine-scale targeted regulations remains inadequate, limiting effective ecological governance, and sustainable regional development. Guided by Social-Economic-Natural Complex Ecosystems (SENCE) theory, this study proposes an analytical framework that integrates ecological security assessment, prediction, and zoning management. The Daqing River Basin, a typical river basin in the North China Plain, was selected as a case study. The results indicate that overall ecological security in the Daqing River Basin improved from a “Moderate” level to a “Relatively Safe” level between 2000 and 2020; however, spatial heterogeneity persisted, with higher ecological security in northwestern and eastern regions and lower ecological security in the central region. Approximately 62% of the Basin experienced an improvement in ecological security level, except in the major urban areas of Beijing, Tianjin, and Hebei provinces, where ecological security deteriorated. From 2025 to 2040, the overall ecological security of the Daqing River Basin is expected to improve and remain at the “Relatively Safe” level. However, spatial heterogeneity will be further aggravated as the ecological security of major urban areas continues to deteriorate. Ecological security management zones and regulation strategies are proposed at the regional and county scales to emphasize integrated regulation for the entire basin and major urban areas. The proposed analytical framework provides valuable insights for advancing theoretical research on ecological security. The case study offers a practical reference for ecological security enhancement in river basins and other regions facing significant human-land conflicts.
Controlled intracellular delivery of biomolecular cargo is critical for developing targeted therapeutics and cell reprogramming. Conventional delivery approaches (e.g., endocytosis of nano-vectors, microinjection, and electroporation) usually require time-consuming uptake processes, labor-intensive operations, and/or costly specialized equipment. Here, we present an acoustofluidics-based intracellular delivery approach capable of effectively delivering various functional nanomaterials to multiple cell types (e.g., adherent and suspension cancer cells). By tuning the standing acoustic waves in a glass capillary, our approach can push cells in flow to the capillary wall and enhance membrane permeability by increasing membrane stress to deform cells via acoustic radiation forces. Moreover, by coating the capillary with cargo-encapsulated nanoparticles, our approach can achieve controllable cell-nanoparticle contact and facilitate nanomaterial delivery beyond Brownian movement. Based on these mechanisms, we have successfully delivered nanoparticles loaded with small molecules or protein-based cargo to U937 and HeLa cells. Our results demonstrate enhanced delivery efficiency compared to attempts made without the use of acoustofluidics. Moreover, compared to conventional sonoporation methods, our approach does not require special contrast agents with microbubbles. This acoustofluidics-based approach creates exciting opportunities to achieve controllable intracellular delivery of various biomolecular cargoes to diverse cell types for potential therapeutic applications and biophysical studies.
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Governments worldwide have implemented non-pharmaceutical interventions (NPIs) to control the spread of coronavirus disease 2019 (COVID-19), and it is crucial to accurately assess the effectiveness of such measures. Many studies have quantified the risk of infection transmission and used simulations to compare the risk before and after the implementation of NPIs to judge policies’ effectiveness. However, the choice of metric used to quantify the risk can lead to different conclusions regarding the effectiveness of a policy. In this study, we analyze the correlation between different transmission-risk metrics, pedestrian environments, and types of infectious diseases using simulation-generated data. Our findings reveal conflicting results among five different metric types in specific environments. More specifically, we observe that, when the randomness of pedestrian trajectories in indoor spaces is low, the closeness centrality exhibits a higher correlation coefficient with infection-based metrics than with contact-based metrics. Furthermore, even within the same pedestrian environment, the likelihood of discrepancies between infection-based metrics and other metrics increases for infectious diseases with low transmission rates. These results highlight the variability in the measured effectiveness of NPIs depending on the chosen metric. To evaluate NPIs accurately, facility managers should consider the type of facility and infectious disease and not solely rely on a single metric. This study provides a simulation model as a tool for future research and improves the reliability of pedestrian-simulation-based NPI effectiveness analysis methods.
With digital coding technology, reconfigurable intelligent surfaces (RISs) become powerful real-time systems for manipulating electromagnetic (EM) waves. However, most automatic RIS designs involve extensive numerical simulations of the unit, including the passive pattern and active devices, requiring high data acquisition and training costs. In addition, for passive patterns, the widely employed random pixelated method presents design efficiency and effectiveness challenges due to the massive pixel combinations and blocked excitation current flow in discrete patterns. To overcome these two critical problems, we propose a versatile RIS design paradigm with efficient topology representation and a separate design architecture. First, a non-uniform rational B-spline (NURBS) is introduced to represent continuous patterns and solve excitation current flow issues. This representation makes it possible to finely tune continuous patterns with several control points, greatly reducing the pattern solution space by 20-fold and facilitating RIS optimization. Then, employing multiport network theory to separate the passive pattern and active device from the unit, the separate design architecture significantly reduces the dataset acquisition cost by 62.5%. Through multistep multiport calculation, the multistate EM responses of the RIS under different structural combinations can be quickly obtained with only one prediction of pattern response, thereby achieving dataset and model reuse for different RIS designs. With a hybrid continuous-discrete optimization algorithm, three examples—including two typical high-performance RISs and an ultra-wideband multilayer RIS—are provided to validate the superiority of our paradigm. Our work offers an efficient solution for RIS automatic design, and the resulting structure is expected to boost RIS applications in the fields of wireless communication and sensing.
Vacuum glazing is highly regarded for its ability to transmit light while providing heat preservation, sound insulation, lightweight characteristics, and resistance to condensation. Scholars have made significant strides in the study of vacuum glazing through their notable efforts. This study systematically reviewed vacuum glazing and its composite structures, including material selection, fabrication techniques, research methods, and performance evaluation. This review initially presented fundamental techniques for preparing vacuum glazing, with a focus on edge seal and support pillar arrangements, and introduced common composite structures such as hybrid and tinted vacuum glazing. Furthermore, this review summarized the analytical, numerical, and experimental methodologies used to assess the thermal performance of vacuum glazing. This study also outlined heat transfer coefficients associated with various vacuum glazing structures, investigated the influence of different parameters on their heat transfer coefficients, and evaluated their potential for energy conservation across diverse climatic regions. Finally, the research delineated future trends in the advancement of vacuum glazing to provide guidance for both theoretical studies and practical applications in industry. This research serves as a valuable resource for both theoretical exploration and practical integration of vacuum glazing, facilitating its improvement and optimization to advance sustainable low-carbon building practices.
Machine learning (ML) has been increasingly adopted to solve engineering problems with performance gauged by accuracy, efficiency, and security. Notably, blockchain technology (BT) has been added to ML when security is a particular concern. Nevertheless, there is a research gap that prevailing solutions focus primarily on data security using blockchain but ignore computational security, making the traditional ML process vulnerable to off-chain risks. Therefore, the research objective is to develop a novel ML on blockchain (MLOB) framework to ensure both the data and computational process security. The central tenet is to place them both on the blockchain, execute them as blockchain smart contracts, and protect the execution records on-chain. The framework is established by developing a prototype and further calibrated using a case study of industrial inspection. It is shown that the MLOB framework, compared with existing ML and BT isolated solutions, is superior in terms of security (successfully defending against corruption on six designed attack scenario), maintaining accuracy (0.01% difference with baseline), albeit with a slightly compromised efficiency (0.231 second latency increased). The key finding is MLOB can significantly enhances the computational security of engineering computing without increasing computing power demands. This finding can alleviate concerns regarding the computational resource requirements of ML-BT integration. With proper adaption, the MLOB framework can inform various novel solutions to achieve computational security in broader engineering challenges.