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[抄録]「サメ革バディバッグ “Peg from mother sea”」を紹介する。近畿大学短期大学部開講の専門科目「演習」 で発案し、企業と協働して開発を進め、実際に販売している商品である。原料に宮城県気仙沼市産ヨシキリザメの皮を使用し、合同会社AtelierShark（アトリエシャーク）が製造・販売している。学生は、市場調査に基づき、コンセプトを設定した上で基本的な商品仕様からネーミングを手がけた。[Abstract] This document reports a “Shark skin Buddy bag “Peg from mother sea.” The students attending “Seminar” at Junior Collage of Kindai University developed the bag in collaboration with a company and brought onto market. The bag with blue shark leather from Kesennuma City, Miyagi Pref. is manufactured and released by AtelierShark LLC. The students were in charge of market research to develop product concept, basic specification, and product’s name.

After a disaster, often roads are damaged and blocked, hindering accessibility for relief efforts. It is essential to dispatch work teams to restore the blocked roads by clearance or repair operations. With the goal of enabling access between critical locations in the disaster area in shortest time, we propose algorithms that determine the schedule and routes of multiple work teams. We minimize the total latency of reaching the critical locations, where the latency of a location is defined as the time it takes from the start of the operation until its first visit by one of the work teams. Coordination among the teams is needed since some blocked edges might be opened by a certain team and utilized by other teams later on. First, we develop an exact mathematical model that handles the coordination requirement. After observing the intractability of this formulation, we introduce two heuristic methods and a lower bounding procedure. In the first method, we develop a mathematical model based on a novel multi-level network representation that yields solutions with disjoint paths. Given that it does not coordinate the teams, we present a matheuristic based on a cluster-first-route-second approach embedded into a local search algorithm together with an additional coordination step to obtain alternative solutions with higher quality and in a shorter time. We test our heuristics on data sets coming from a real network from the literature (180 instances) and randomly generated ones (640 instances) and observe the superiority of the solutions obtained by incorporation of coordination., NA

In this paper, we study the employment of multiple UAVs to monitor Points of Interests (PoIs) in a disaster area, e.g., collapsed buildings after an earthquake, where the UAVs can take photos /videos for the people trapped at the PoIs. Unlike most existing studies that ignored the monitoring time of PoIs and simply minimized the longest flying distance of the UAVs, we observe that it takes some time to monitor a PoI. Then, although the flying distance of a UAV in its flying tour is not long at all, the tour may contain many densely-located PoIs and it thus will take a very long time for the UAV to monitor the PoIs in the tour. In this paper we consider both the UAV flying time and the PoI monitoring time. We then propose novel approximation algorithms for the problem of finding flying tours for K UAVs to visit all PoIs, such that the maximum spent time of the K tours is minimized. We finally evaluate the performance of the proposed algorithms via simulation environments, and experimental results show that the maximum tour times by the proposed algorithms are up to 30% shorter than those by existing algorithms.

In this paper we study the deployment of multiple unmanned aerial vehicles (UAVs) to form a temporal UAV network for the provisioning of emergent communications to affected people in a disaster zone, where each UAV is equipped with a lightweight base station device and thus can act as an aerial base station for users. Unlike most existing studies that assumed that a UAV can serve all users in its communication range, we observe that both computation and communication capabilities of a single lightweight UAV are very limited, due to various constraints on its size, weight, and power supply. Thus, a single UAV can only provide communication services to a limited number of users. We study a novel problem of deploying $K$ UAVs in the top of a disaster area such that the sum of the data rates of users served by the UAVs is maximized, subject to that (i) the number of users served by each UAV is no greater than its service capacity; and (ii) the communication network induced by the $K$ UAVs is connected. We then propose a $\frac{1-1/e}{\lfloor \sqrt{K} \rfloor}$-approximation algorithm for the problem, improving the current best result of the problem by five times (the best approximation ratio so far is $\frac{1-1/e}{5( \sqrt{K} +1)}$), where $e$ is the base of the natural logarithm. We finally evaluate the algorithm performance via simulation experiments. Experimental results show that the proposed algorithm is very promising. Especially, the solution delivered by the proposed algorithm is up to 12% better than those by existing algorithms., 14 pages, this paper was submitted to the journal of IEEE/ACM Transactions on Networking

The importance of an optimal solution for disaster evacuation has recently raised attention from researchers across multiple disciplines. This is not only a serious, but also a challenging task due to the complexities of the evacuees’ behaviors, route planning, and demanding coordination services. Although existing studies have addressed these challenges to some extent, mass evacuation in natural disasters tends to be difficult to predict and manage due to the limitation of the underlying models to capture realistic situations. It is therefore desirable to have on-demand mechanisms of locally-driven computing and data exchange services in order to enable near real-time capture of the disaster area during the evacuation. For this purpose, this paper comprehensively surveys recent advances in information and communication technology-enabled disaster evacuations, with the focus on fog computation and communication services to support a massive evacuation process. A numerous variety of tools and techniques are encapsulated within a coordinated on-demand strategy of an evacuation platform, which is aimed to provide a situational awareness and response. Herein fog services appear to be one of the viable options for responsive mass evacuation because they enable low latency data processing and dissemination. They can additionally provide data analytics support for autonomous learning for both the short-term guidance supports and long-term usages. This work extends the existing data-oriented framework by outlining comprehensive functionalities and providing seamless integration. We review the principles, challenges, and future direction of the state-of-the-art strategies proposed to sit within each functionality. Taken together, this survey highlights the importance of adaptive coordination and reconfiguration within the fog services to facilitate responsive mass evacuations as well as open up new research challenges associated with analytics-embedding network and computation, which is critical for any disaster recovery activities.

Disaster management generally includes the post-disaster stage, which consists of the actions taken in response to the disaster damages. These actions include the employment of emergency plans and assigned resources to (i) rescue affected people immediately, (ii) deliver personnel, medical care and equipment to the disaster area, and (iii) aid to prevent the infrastructural and environmental losses. In the response phase, humanitarian logistics directly influence the efficiency of the relief operation. Ambulances routing problem is defined as employing the optimisation tools to manage the flow of ambulances for finding the best ambulance tours to transport the injured to hospitals. Researchers pointed out the importance of equity and fairness in humanitarian relief services: managing the operations of ambulances in the immediate aftermath of a disaster must be done impartially and efficiently to rescue affected people with different priority in accordance with the restrictions. Our research aim is to find the best ambulance tours to transport the patients during a disaster in relief operations while considering fairness and equity to deliver services to patients in balance. The problem is formulated as a new variant of the team orienteering problem with hierarchical objectives to address also the efficiency issue. Due to the limitation of solving the proposed model using a general-purpose solver, we propose a new hybrid algorithm based on a machine learning and neighbourhood search. Based on a new set of realistic benchmark instances, our quantitative analysis proves that our algorithm is capable to largely reduce the solution running time especially when the complexity of the problem increases. Further, a comparison between the fair solution and the system optimum solution is also provided.

The goaf is the main disaster area for natural fires in mines, and liquid nitrogen has become an essential medium for fire prevention in goafs due to its excellent cooling and inerting properties.A...