Japanese electronics giant Fujitsu said Thursday that it and Nagoya University have jointly developed an artificial intelligence-based simulation technology aimed at promoting ride-hailing services. The two sides aim to offer the new technology to local governments struggling to secure means of transportation for elderly residents and business operators facing a shortage of drivers. They plan to put the technology into practical use in fiscal 2026. Among factors hindering the spread of emerging mobility services, including ride-hailing and demand-responsive ride-sharing taxis, are psychological hurdles such as people's unfamiliarity with how to use them. Fujitsu and Nagoya University will in digital space analyze problems based on resident surveys and find effective methods to increase utilization rate, leveraging an AI technology that predicts human behavior.

In areas that are inaccessible to humans, such as the lunar surface and landslide sites, there is a need for multiple autonomous mobile robot systems that can replace human workers. In particular, at landslide sites such as river channel blockages, robots are required to remove water and sediment from the site as soon as possible. Conventionally, several construction machines have been deployed to the site for civil engineering work. However, because of the large size and weight of conventional construction equipment, it is difficult to move multiple units of construction equipment to the site, resulting in significant transportation costs and time. To solve such problems, this study proposes a novel growing robot by eating environmental material called GREEMA, which is lightweight and compact during transportation, but can function by eating on environmental materials once it arrives at the site. GREEMA actively takes in environmental materials such as water and sediment, uses them as its structure, and removes them by moving itself. In this paper, we developed and experimentally verified two types of GREEMAs. First, we developed a fin-type swimming robot that passively takes water into its body using a water-absorbing polymer and forms a body to express its swimming function. Second, we constructed an arm-type robot that eats soil to increase the rigidity of its body. We discuss the results of these two experiments from the viewpoint of Explicit-Implicit control and describe the design theory of GREEMA.

Geoparsing is a fundamental technique for analyzing geo-entity information in text. We focus on document-level geoparsing, which considers geographic relatedness among geo-entity mentions, and presents a Japanese travelogue dataset designed for evaluating document-level geoparsing systems. Our dataset comprises 200 travelogue documents with rich geo-entity information: 12,171 mentions, 6,339 coreference clusters, and 2,551 geo-entities linked to geo-database entries.

In the hills between Kyoto, Osaka and Nara prefectures, surrounded by technology companies and startups, Spread Co. is preparing to open the world's largest automated leaf-vegetable factory. It's the company's second vertical farm and could mark a turning point for vertical farming -- keeping the cost low enough to compete with traditional farms on a large scale. For decades, vertical farms that grow produce indoors without soil in stacked racks have been touted as a solution to rising food demand in the world's expanding cities. The problem has always been reproducing the effect of natural rain, soil and sunshine at a cost that makes the crop competitive with traditional agriculture. Spread is among a handful of commercial firms that claim to have cracked that problem using a mix of robotics, technology and scale.

MUMBAI – Mobile apps that help women in the Indian countryside and tiny villages in Japan to open their homes to visitors from across the world are generating incomes, revitalizing remote communities and helping to curb migration to cities. A women's organization in the western Indian state of Gujarat has tied up with Airbnb, the short-term home rental service, to train rural women to be hosts and list their homes on its site. A year in, the number of women earning from home sharing has doubled, according to the Self Employed Women's Association (SEWA), which has about 2 million members, mostly in villages. "At first, we weren't sure how the women would fare and if people would respond to home stays in these areas," said Reema Nanavaty, a director at SEWA. "But once they began getting guests, the women invested in upgrading their homes and started using Google Translate to communicate with guests. It has become a significant source of income for them," she said.