A beautifully illustrated single-project monograph on the innovative design process and creation of a flagship lakeside resort in central China, the Hilton Wuhan Optics Valley resort, this book showcases the chronological project phases, from the early-stage site preparations, design and engineering parameters, through to final construction and completion. The resort is a business and convention center, as well as a prime hub for political and business activities. There are dedicated spaces for meetings and receptions, a full suite of leisure facilities, such as a large spa area, an indoor heated swimming pool, an outdoor swimming pool, a gym, a cycling route, a lakeside basketball court, and a tennis court. The hotel component of the resort comprises luxury guest rooms and suites, all with private balconies overlooking a beautiful lake, a convention centre with a huge zero-pillar banquet hall, and an outdoor ceremonial lawn. Hilton Wuhan Optics Valley is featured by its innovative design. Tightly knit around the core site, the layout is characterised by a central symmetry and a clear separation of the external and the internal areas. The creative use of a cluster of courtyards interlacing each other characterises the hotel lobby. The functional areas are thus separated so that the guests can enjoy an experience of unique spaces typically offered only by small hotels. The design of the facade drew inspiration from Jing-chu culture clean lines, delicate details, traditional textures and natural materials and imparts a sense of understated luxury and otherworldly elegance, allowing the architecture of the hotel to perfectly blend into the natural environment around Yanxi Lake. This book is a unique reference and useful guide for architects, engineers and designers of resorts, or related typologies.

This article studies constructions of reproducing kernel Banach spaces (RKBSs) which may be viewed as a generalization of reproducing kernel Hilbert spaces (RKHSs). A key point is to endow Banach spaces with reproducing kernels such that machine learning in RKBSs can be well-posed and of easy implementation. First the authors verify many advanced properties of the general RKBSs such as density, continuity, separability, implicit representation, imbedding, compactness, representer theorem for learning methods, oracle inequality, and universal approximation. Then, they develop a new concept of generalized Mercer kernels to construct $p$-norm RKBSs for $1\leq p\leq\infty$.