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Waterproof adhesion materials (WAL) for steel bridge decks should have comprehensive strong bonding capabilities to steel substrates/pavement layers, water resistance, and persistence to harsh conditions. Methacrylate-based adhesive (MMA), as a typical WAL material for steel bridge decks, lacks sufficient flexibility and diverse compatibility. In this article, flexible, stabile at high temperature, and well-bonded poly (methyl methacrylate)/n-butyl acrylate copolymer [poly(MMA-r-nBA)] coatings were developed with toughening as a breakthrough point. The above properties are obtained by forming an interpenetrating network through interchain van der Waals forces. In addition to the flexibility provided by the long-chain structure of n-butyl acrylate, its unique molecular structure allows it to improve the high-temperature stability and lower the glass transition temperature of MMA. Due to the reduced glass transition temperature, it can also be matched to various pavement layers. Thus, the poly(MMA-r-nBA) composite polymer coatings can be applied to steel bridge decks that require long-term service in various conditions.

When holding visual information temporarily in working memory (WM), the neural representation of the memorandum is distributed across various cortical regions, including visual and frontal cortices. However, the role of stimulus representation in visual and frontal cortices during WM has been controversial. Here, we tested the hypothesis that stimulus representation persists in the frontal cortex to facilitate flexible control demands in WM. During functional MRI, participants flexibly switched between simple WM maintenance of visual stimulus or more complex rule-based categorization of maintained stimulus on a trial-by-trial basis. Our results demonstrated enhanced stimulus representation in the frontal cortex that tracked demands for active WM control and enhanced stimulus representation in the visual cortex that tracked demands for precise WM maintenance. This differential frontal stimulus representation traded off with the newly-generated category representation with varying control demands. Simulation using multi-module recurrent neural networks replicated human neural patterns when stimulus information was preserved for network readout. Altogether, these findings help reconcile the long-standing debate in WM research, and provide empirical and computational evidence that flexible stimulus representation in the frontal cortex during WM serves as a potential neural coding scheme to accommodate the ever-changing environment.

This review proposes a novel integration of game-theoretical methods—specifically Evolutionary Game Theory (EGT), Stackelberg games, and Bayesian games—with deep reinforcement learning (DRL) to optimize electricity markets. Our approach uniquely addresses the dynamic interactions among power purchasing and generation enterprises, highlighting both theoretical underpinnings and practical applications. We demonstrate how this integrated framework enhances market resilience, informs evidence-based policy-making, and supports renewable energy expansion. By explicitly connecting our findings to regulatory strategies and real-world market scenarios, we underscore the political implications and applicability of our results in diverse global electricity systems. By integrating EGT with advanced methodologies such as DRL, this study develops a comprehensive framework that addresses both the dynamic nature of electricity markets and the strategic adaptability of market participants. This hybrid approach allows for the simulation of complex market scenarios, capturing the nuanced decision-making processes of enterprises under varying conditions of uncertainty and competition. The review systematically evaluates the effectiveness and cost-efficiency of various control policies implemented within electricity markets, including pricing mechanisms, capacity incentives, renewable integration incentives, and regulatory measures aimed at enhancing market competition and transparency. Our analysis underscores the potential of EGT to significantly enhance market resilience, enabling electricity markets to better withstand shocks such as sudden demand fluctuations, supply disruptions, and regulatory changes. Moreover, the integration of EGT with DRL facilitates the promotion of sustainable energy integration by modeling the strategic adoption of renewable energy technologies and optimizing resource allocation. This leads to improved overall market performance, characterized by increased efficiency, reduced costs, and greater sustainability. The findings contribute to the development of robust regulatory frameworks that support competitive and efficient electricity markets in an evolving energy landscape. By leveraging the dynamic and adaptive capabilities of EGT and DRL, policymakers can design regulations that not only address current market challenges but also anticipate and adapt to future developments. This proactive approach is essential for fostering a resilient energy infrastructure capable of accommodating rapid advancements in renewable technologies and shifting consumer demands. Additionally, the review identifies key areas for future research, including the exploration of multi-agent reinforcement learning techniques and the need for empirical studies to validate the theoretical models and simulations discussed. This study provides a comprehensive roadmap for optimizing electricity markets through strategic and policy-driven interventions, bridging the gap between theoretical game-theoretic models and practical market applications.

The canonical approach to exploring brain-behaviour relationships is to group individuals according to a phenotype of interest, and then explore the neural correlates of this grouping. A limitation of this approach is that multiple aetiological pathways could result in a similar phenotype, so the role of any one brain mechanism may be substantially underestimated. Building on advances in network analysis, we used a data-driven community-clustering algorithm to identify robust subgroups based on white-matter microstructure in childhood and adolescence (total N = 313, mean age: 11.24 years). The algorithm indicated the presence of two equal-size groups that show a critical difference in fractional anisotropy (FA) of the left and right cingulum. Applying the brain-based grouping in independent samples, we find that these different ‘brain types’ had profoundly different cognitive abilities with higher performance in the higher FA group. Further, a connectomics analysis indicated reduced structural connectivity in the low FA subgroup that was strongly related to reduced functional activation of the default mode network. These results provide a proof-of-concept that bottom-up brain-based groupings can be identified that relate to cognitive performance. This provides a first demonstration of a complimentary approach for investigating individual differences in brain structure and function, particularly for neurodevelopmental disorders where researchers are often faced with phenotypes that are difficult to define at the cognitive or behavioural level.

By collecting the publication data of scientists belonging to China’s Project 985 universities in the chemistry field and classifying the scientists into Distinguished Young Scholars (DYSs) and non-Distinguished Young Scholars (non-DYSs), this study constructed scientists’ ego research collaboration networks and compared the network differences between DYSs and non-DYSs, who usually occupy different structural positions in the science community. We employed three network indicators (degree centrality, betweenness centrality and tie strength) to measure the advantages related to network locations. Then, we investigated and compared DYSs’ and non-DYSs’ capability of using the social capital embedded in their research collaboration networks to improve their research performance. The results show that DYSs exhibit the better capability to use social capital from research collaboration networks and that their Ph.D. mentors may be a critical factor in scientific success. We further discussed the theoretical and practical implications at the end of this study.

The near-infrared photodetection of monolayer MoS2 can be achieved using upconverted nanoparticles (UCNPs). Herein, we demonstrated that gold nanorods (Au NRs) further enhanced the near-infrared photoresponsivity of a hybrid device via the surface plasmon enhancement of the localized field. We synthesized a three-layer device comprising Au NRs, UCNPs (NaYF4:Yb3+, Er3+), and monolayer MoS2, and examined its photoelectric characteristics using laser irradiation with varying power densities at 980 nm, the excitation wavelength of UCNPs. Compared with a device without Au NRs, the photoelectric response of the three-layer device was greatly improved at 5 V bias, and photoresponsivity was increased at visible wavelengths (450, 532, and 635 nm). This study contributes to the knowledge of two-dimensional materials for the development of hybrid photoelectronic devices.

The decks of steel–concrete composite bridges are constantly exposed to severe environmental conditions, which frequently give rise to significant issues, including cracks and holes. These problems occur due to the formation of blisters under the paving layer with waterproofing membranes. This paper aims to delve into the characteristics of blisters during their expansion and propagation stages. Additionally, it proposes a rating index and a simplified calculation formula to assess the interface propagation performance of bridge deck pavement. To achieve this, the research group developed a simulated blister test device and employed the digital image correlation (DIC) technique. The study investigated the impact of pavement structure, waterproofing layer, and air voids on blister propagation behavior. It was discovered that the pavement blister test encompassed two distinct stages: expansion and propagation. Furthermore, the SMA-13 asphalt mixture exhibited slightly superior resistance to blistering compared to AC-13. It was also observed that when the mixture void ratio is less than 3.5%, it becomes more susceptible to blistering deformation, ultimately leading to debonding damage. Among the waterproofing materials tested, SBS-modified emulsified asphalt demonstrated the weakest adhesion to cement concrete substrates, while SBS-modified asphalt performed slightly better than rubberized asphalt.

To investigate the dynamic characteristics of early fish life stages and their association with environmental factors in Xiangshan Bay, China, two surveys were conducted in the springs of 2023 and 2024 to collect fish eggs and larvae. A total of 21,221 eggs and 10,730 larvae of 23 species belonging to 5 orders and 13 families were collected. Dominant species identified from eggs included Scomberomorus niphonius, Konosirus punctatus, and Pennahia argentata, while dominant species of larvae included Engraulis japonicus, Planiliza haematocheilus, and Rhinogobius giurinus. Species diversity analysis showed that the diversity was higher in spring 2023 (H′ = 1.13) but decreased in spring 2024 due to the outbreak of dominant species (H′ = 0.86). Cluster analysis and non-metric multidimensional scaling ordination analysis revealed significant regional differentiation in the community structure of fish eggs and larvae between the springs of 2023 and 2024. Mantel test correlation and redundancy analyses indicated that temperature, salinity, dissolved oxygen, and chlorophyll-a concentration were the main environmental factors influencing the distribution of fish eggs and larvae. This study elucidates spatio-temporal patterns and environmental response mechanisms of early fish life stages in Xiangshan Bay, providing scientific support for the conservation and sustainable utilization of fishery resources in the National Fishery Germplasm Resource Reserve.

Cancer is the second leading cause of death worldwide. Its incidence has been increasing in recent years, and it is becoming a major threat to human health. Conventional cancer treatment strategies, including surgery, chemotherapy, and radiotherapy, have faced problems such as drug resistance, toxic side effects and unsatisfactory therapeutic efficacy. Therefore, better development and utilization of biomaterials can improve the specificity and efficacy of tumor therapy. Algae, as a novel living material, possesses good biocompatibility. Although some reviews have elucidated several algae-based biomaterials for cancer treatment, the majority of the literature has focused on a limited number of algae. As a result, there is currently a lack of comprehensive reviews on the subject of anticancer algae. This review aims to address this gap by conducting a thorough examination of algal species that show potential for anticancer activity. Furthermore, our review will also elucidate the engineering strategies of algae and discuss the challenges and prospects associated with their implementation.Cancer is the second leading cause of death worldwide. Its incidence has been increasing in recent years, and it is becoming a major threat to human health. Conventional cancer treatment strategies, including surgery, chemotherapy, and radiotherapy, have faced problems such as drug resistance, toxic side effects and unsatisfactory therapeutic efficacy. Therefore, better development and utilization of biomaterials can improve the specificity and efficacy of tumor therapy. Algae, as a novel living material, possesses good biocompatibility. Although some reviews have elucidated several algae-based biomaterials for cancer treatment, the majority of the literature has focused on a limited number of algae. As a result, there is currently a lack of comprehensive reviews on the subject of anticancer algae. This review aims to address this gap by conducting a thorough examination of algal species that show potential for anticancer activity. Furthermore, our review will also elucidate the engineering strategies of algae and discuss the challenges and prospects associated with their implementation.

This study proposes a risk measurement approach to assess collision risks in mixed traffic flow, focusing on the integrated behavior of car-following and lane-changing. A new surrogate safety measure (SSM), denoted as Rtotal, is developed to provide a comprehensive risk assessment. Numerical analysis is used to determine the weights of parameters within Rtotal, and its validity is substantiated using an empirical dataset, with a risk threshold of 0.49 established when the time to collision (TTC) is set to 2 s. The study incorporates scenarios of connected and automated vehicle (CAV) degradation and evaluates the influence of penetration rates, perception–reaction time (PRT), and lane-changing modes on risk levels. Simulation results reveal that a CAV penetration rate between 0.4 and 0.6 represents a critical range where collision risks significantly increase, reflecting safety dynamics under CAV degradation. Furthermore, in scenarios involving lane-changing, the degradation of the following vehicle in the target lane poses the highest risk. At lower PRTs, the penetration rate exerts a more significant influence on collision risks. Rtotal has been validated across various scenarios, showing strong applicability and more sensitive trends than other SSMs, making it well-suited for assessing long-term comprehensive traffic flow risks. These findings offer practical guidance for traffic management to establish real-time risk prediction and warning systems for identifying high-risk car-following and lane-changing behaviors. Future research can explore the applicability of the proposed risk index in more complex traffic scenarios and its effectiveness across different levels of vehicle automation and connectivity.