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Cancer is one of the leading causes of death worldwide, and with the continuous development of life sciences and pharmaceutical technology, more and more antitumor drugs are being used in clinics to benefit cancer patients. However, the incidence of chemotherapy-induced cardiotoxicity has been continuously increasing, threatening patients’ long-term survival. Cardio-oncology has become a research hot spot, and the combination of nanotechnology and biomedicine has brought about an unprecedented technological revolution. Nanomaterials have the potential to maximize the efficacy and reduce the side effects of chemotherapeutic drugs when used as their carriers, and several nano-formulations of frequently used chemotherapeutic drugs have already been approved for marketing. In this review, we summarize chemotherapeutic drugs that are highly associated with cardiotoxicity and evaluate the role of nano-delivery systems in reducing cardiotoxicity based on studies of their marketed or R&D nano-formulations. Some of the marketed chemotherapy drugs are combined with nano-delivery systems that can effectively deliver chemotherapy drugs to tumors and cannot easily penetrate the endothelial barrier of the heart, thus decreasing their distribution in the heart and reducing the cardiotoxicity to some extent. However, many chemotherapy nanomedicines that are marketed or in R&D have not received enough attention in determining their cardiotoxicity. In general, nanomedicine is an effective method to reduce the cardiotoxicity of traditional chemotherapy drugs. However, cardiovascular complications in cancer treatment are very complex diseases, requiring the application of multiple measures to achieve effective management and prevention.

With the development of the room rental market, many room rental websites have been created, e.g., SpareRoom and EasyRoommate. On these websites, people find not only rooms for rent but also suitable roommates. Inspired by the rental mode in practice, a benchmark room allocation model was introduced by Chan et al., in which 2n agents must be allocated to n rooms that have the same capacity and each agent can be allocated to any room. However, in practice, rooms may differ in terms of capacity, e.g., college dorms or apartments may contain both two-bed rooms and four-bed rooms. Moreover, an agent can only be allocated to a room of which the rent does not exceed the agent's budget. In this scenario, we must consider not only the agents' preferences but also the capacity diversity of the rooms and the budget constraints while allocating the rooms. Therefore, this paper investigates the room allocation problem with capacity diversity and budget constraints. We mainly focus on finding an allocation that maximizes social welfare. First, this paper demonstrates that finding an allocation that maximizes the social welfare is NP-hard (i.e., non-deterministic polynomial-time hard), even if only one room's capacity is larger than 1 and the other rooms' capacities are all 1. Second, this paper presents a (c* + 2)/2 + ε-factor approximation algorithm (with ε > 0) for the case in which the capacity of each room does not exceed a constant c*. Third, this paper proposes a heuristic algorithm based on the local search for the general case in which the capacity of each room is not bounded by a constant. The experimental results demonstrate that the proposed algorithm can produce near-optimal solutions. Finally, this paper investigates how to find a roommate stable or room envyfree allocation with a social welfare guarantee.

Delivery service sharing (DSS) has made an important contribution in the optimization of daily order delivery applications. Existing DSS algorithms introduce two major limitations. First, due to computational reasons, most DSS algorithms focus on the fixed pickup/drop-off time scenario, which is inconvenient for real-world scenarios where customers can choose the pickup/drop-off time flexibly. On the other hand, to address the intractable DSS with the flexible time windows (DSS-Fle), local search-based heuristics are widely employed; however, they have no theoretical results on the advantage of order sharing. Against this background, this paper designs a novel algorithm for DSS-Fle, which is efficient on both time complexity and system throughput. Inspired by the efficiency of shareability network on the delivery service routing (DSR) variant where orders cannot be shared and have the fixed time window, we first consider the variant of DSR with flexible time windows (DSR-Fle). For DSR-Fle, the order’s flexible time windows are split into multiple virtual fixed time windows, one of which is chosen by the shareability network as the order’s service time. On the other hand, inspired by efficiency of local search heuristics, we further consider the variant of DSS with fixed time window (DSS-Fix). For DSS-Fix, the beneficial sharing orders are searched and inserted to the shareability network. Finally, combining the spitting mechanism proposed in DSR-Fle and the inserting mechanism proposed in DSS-Fix together, an efficient algorithm is proposed for DSS-Fle. Simulation results show that the proposed DSS-Fle variant algorithm can scale to city-scale scenarios with thousands of regions, orders and couriers, and has the significant advantage on improving system throughput.