Recent advances in inverse‐design approaches for discovering optical structures based on desired functional characteristics have reshaped the landscape of nanophotonic structures, where most studies have focused on how light interacts with nanophotonic structures only. When quantum emitters (QEs), such as atoms, molecules, and quantum dots, are introduced to couple to the nanophotonic structures, the light–matter interactions become much more complicated, forming a rapidly developing field – quantum nanophotonics. Typical quantum functional characteristics depend on the intrinsic properties of the QE and its electromagnetic environment created by the nanophotonic structures, commonly represented by a scalar quantity, local‐density‐of‐states (LDOS). In this work, we introduce a generalized inverse‐design framework in quantum nanophotonics by taking LDOS as the bridge to connect the nanophotonic structures and the quantum functional characteristics. We take a simple system consisting of QEs sitting on a single multilayer shell–metal–nanoparticle (SMNP) as an example, apply fully‐connected neural networks to model the LDOS of SMNP, inversely design and optimize the geometry of the SMNP based on LDOS, and realize desirable quantum characteristics in two quantum nanophotonic problems: spontaneous emission and entanglement. Our work introduces deep learning to the quantum optics domain for advancing quantum device designs; and provides a new platform for practicing deep learning to design nanophotonic structures for complex problems without a direct link between structures and functional characteristics.

The relationship among stakeholders is complicated and full of collaboration barriers, which makes urban renewal an intersection of various contradictions. However, the existing literature considers the barriers to urban renewal independent of stakeholders, and the interaction between multiple stakeholders and barriers to collaboration has been ignored. Therefore, this study uses a literature review and expert interviews to identify stakeholders and their collaboration barriers in the process of urban renewal. Based on the results of expert questionnaires, a two-mode network model of stakeholder–collaboration barrier is constructed to clarify the complex interaction and reveal the power and status of stakeholders in a network relationship. The study found that each barrier was associated with at least three stakeholders, indicating the necessity of stakeholders to establish partnerships. Further analysis shows that the government, local and other administrative organizations, consulting parties, and developers are the most influential stakeholders. The vague boundary of property rights, lack of expert advice and expertise, and different stakeholder awareness were identified as key barriers affecting sustainable collaboration. Finally, this study proposes and validates five strategies to promote collaboration among stakeholders. This study helps practitioners identify the priority problems to be solved under limited resources and provides effective measures to promote stakeholder collaboration.

Renting is, like owning a house, a way to realize residence rights, playing an important role in maintaining the equilibrium of the housing market. The lack of attention paid to policy design of the rental housing market causes low effectiveness in the housing resource flow and allocation at both local and national levels. Thus, we propose a novel design framework and process of public policy, in particular the development policy for the rental housing market. This innovative approach abstracts the policy design process into a solution-formation process for a high-dimensional and multi-objective optimization problem. First, based on opinion mining, using co-occurrence networks, text mining and other methods, in addition to authoritative literature and expert opinions from the Chinese Social Sciences Citation Index (CSSCI) as data sources, the objective function and the constraint function coefficients were determined to construct a multi-objective function of rental housing market policy. Second, this paper proposes a two-stage evolutionary high-dimensional multi-objective optimization algorithm based on the Pareto dominance relationship to solve high-dimensional multi-objective functions. Finally, we designed a rental housing policy tool-mix selection system-modeling process and obtained six sets of feasible solutions and objectives after 300,000 simulations. Therefore, the policy tool-mix selection system presented in this study effectively supports the policymaking process.

Recycled water is considered as a viable alternative water source, and its use is of great significance in dealing with the shortage of water resources. However, it is often rejected by the public during its promotion. To identity the common social attributes of proponents of recycled water, it is essential to study the profiled customers’ willingness to accept recycled water during the most difficult initial stage of promoting recycled water use projects. A meta-analysis was conducted in this study to deal with data concerning the influence of different social demographic factors on the public’s acceptance of recycled water use. Three steps of meta-analysis were mainly used in this paper. First, a random-effect model was used to measure the effect size of influencing factors, such as age, gender, and education. It was found that younger women were more likely to accept recycled water use than older men, and individuals with higher education were more willing to accept recycled water. Then, a cumulative meta-analysis showed that it was feasible to select young women with higher education as target profile customers in the initial stage of recycled water promotion. Finally, according to a meta-regression analysis, it was revealed that different research areas and selected model methods have important regulatory effects on the intention of the target population to use recycled water.

The mismatch between the supply and demand of online-listed rental housing (ORH) is an important factor restricting the operational efficiency of online rental service platforms. However, extant literature pays little attention to this problem. This study proposes an ORH multiattribute supply and demand matching decision model based on the perceived utility of matching both sides of this market. The model considers the multiattribute information of ORH, such as area, transportation, rent, room, and interior decoration, and quantifies their perceived utility values based on the theory of disappointment. Thereafter, we construct the matching decision model and verify it for feasibility by applying it to Shanghai’s ORH supply and demand information—our empirical case. The results show that this method can be applied to online rental housing platforms and meet the supply and demand matching requirements to the greatest extent. The constructed model takes into account the perceptions of both supply and demand parties, may promote the effective matching of ORH supply and demand, and bears theoretical implications for the improvement of rental housing matching in ORH platforms.