A Russian drone and missile attack on the Ukrainian capital Kyiv has killed at least one person and injured seven others, city officials say. Early on Saturday morning residential buildings in several districts were hit and loud explosions could be heard across the city. Kyiv's mayor Vitaly Klitschko said a 13-year-old child was among the injured and four people had been taken to hospital. Earlier this week a similar attack on Kyiv killed seven people, Ukrainian officials said. The latest bombardment came as Ukrainian negotiators were preparing for talks with US officials this weekend on an amended US peace plan.

Generative AI offers new opportunities for automating urban planning by creating site-specific urban layouts and enabling flexible design exploration. However, existing approaches often struggle to produce realistic and practical designs at scale. Therefore, we adapt a state-of-the-art Stable Diffusion model, extended with ControlNet, to generate high-fidelity satellite imagery conditioned on land use descriptions, infrastructure, and natural environments. To overcome data availability limitations, we spatially link satellite imagery with structured land use and constraint information from OpenStreetMap. Using data from three major U.S. cities, we demonstrate that the proposed diffusion model generates realistic and diverse urban landscapes by varying land-use configurations, road networks, and water bodies, facilitating cross-city learning and design diversity. We also systematically evaluate the impacts of varying language prompts and control imagery on the quality of satellite imagery generation. Our model achieves high FID and KID scores and demonstrates robustness across diverse urban contexts. Qualitative assessments from urban planners and the general public show that generated images align closely with design descriptions and constraints, and are often preferred over real images. This work establishes a benchmark for controlled urban imagery generation and highlights the potential of generative AI as a tool for enhancing planning workflows and public engagement.

A new bill moving through the Florida Senate would give homeowners the right to use "reasonable force" to take down drones infringing on their right to privacy, directly conflicting with federal airspace regulations while raising new legal questions regarding how far a person can go to defend their home from surveillance. The bill primarily focuses on further regulating the use of unmanned aircraft systems (UAS) while broadening the scope of locations that are protected from drone flights within the state, such as airports and correctional facilities. Notably, the bill would permit homeowners to use "reasonable force" to stop a drone from infringing on their expectation of privacy. A bill proposed in the Florida Senate would allow homeowners to use "reasonable force" to take down drones infringing on their right to privacy. "No one wants to have a drone sitting over their property, filming what they do for any number of reasons," Florida-based attorney Raul Gastesi told Fox News Digital.

The early detection and prediction of health status decline among the elderly at the neighborhood level are of great significance for urban planning and public health policymaking. While existing studies affirm the connection between living environments and health outcomes, most rely on single data modalities or simplistic feature concatenation of multi-modal information, limiting their ability to comprehensively profile the health-oriented urban environments. To fill this gap, we propose CureGraph, a contrastive multi-modal representation learning framework for urban health prediction that employs graph-based techniques to infer the prevalence of common chronic diseases among the elderly within the urban living circles of each neighborhood. CureGraph leverages rich multi-modal information, including photos and textual reviews of residential areas and their surrounding points of interest, to generate urban neighborhood embeddings. By integrating pre-trained visual and textual encoders with graph modeling techniques, CureGraph captures cross-modal spatial dependencies, offering a comprehensive understanding of urban environments tailored to elderly health considerations. Extensive experiments on real-world datasets demonstrate that CureGraph improves the best baseline by $28\%$ on average in terms of $R^2$ across elderly disease risk prediction tasks. Moreover, the model enables the identification of stage-wise chronic disease progression and supports comparative public health analysis across neighborhoods, offering actionable insights for sustainable urban development and enhanced quality of life. The code is publicly available at https://github.com/jinlin2021/CureGraph.

While official reports of eerie drone-like UFOs dropped over the holidays, New Jersey residents are still coming forward with bizarre encounters. Two witnesses in Manalapan Township, for example, videotaped a bus-sized, 25- to 50-foot-long black triangle UFO that they saw'pull off a high g [force] maneuver over a residential area' just days before Christmas. The sighting, which lasted at least one minute, ended with the object zooming'in the general direction of McGuire [Joint Base McGuire-Dix-Lakehurst]' -- matching a persistent pattern of'drone' UFO incursions over US bases in recent years. Another New Jersey skywatcher recorded what they described as a classic'flying saucer' with an'aura or haze around object' just three miles off the coast of Atlantic City. And still more Garden State witnesses now say they saw as many as 20 to 30 drones just this Wednesday night, which'kind of hovered and all looked like miniature aircraft,' in an account posted to Facebook.

Israel killed three Palestinians in a drone strike on Thursday in Tulkarem, a city and refugee camp in the occupied West Bank. That was during an Israeli raid – a near-daily occurrence in the West Bank – on the Tulkarem refugee camp, during which Israeli troops clashed with fighters from the Qassam Brigades, the military wing of Hamas, according to fighters in the city. Here's all you need to know about Israeli raids on Tulkarem: News reports say Israeli soldiers were deployed on rooftops and sent bulldozers into the camp to destroy large residential areas. Israel also reportedly set fire to people's homes and prevented local relief workers from putting the fires out. Experts say Israel's tactics during its raids appear to be part of a broader doctrine to collectively punish the population, ostensibly because pockets of armed resistance are fighting back against Israel's ever-entrenching occupation. Israel claims that it is conducting "counter-terrorism" operations.

Participatory urban planning is the mainstream of modern urban planning that involves the active engagement of residents. However, the traditional participatory paradigm requires experienced planning experts and is often time-consuming and costly. Fortunately, the emerging Large Language Models (LLMs) have shown considerable ability to simulate human-like agents, which can be used to emulate the participatory process easily. In this work, we introduce an LLM-based multi-agent collaboration framework for participatory urban planning, which can generate land-use plans for urban regions considering the diverse needs of residents. Specifically, we construct LLM agents to simulate a planner and thousands of residents with diverse profiles and backgrounds. We first ask the planner to carry out an initial land-use plan. To deal with the different facilities needs of residents, we initiate a discussion among the residents in each community about the plan, where residents provide feedback based on their profiles. Furthermore, to improve the efficiency of discussion, we adopt a fishbowl discussion mechanism, where part of the residents discuss and the rest of them act as listeners in each round. Finally, we let the planner modify the plan based on residents' feedback. We deploy our method on two real-world regions in Beijing. Experiments show that our method achieves state-of-the-art performance in residents satisfaction and inclusion metrics, and also outperforms human experts in terms of service accessibility and ecology metrics.

JINSP(Jiutian Intelligence Network Simulation Platform) describes a series of basic emulators and their combinations, such as the simulation of the protocol stack for dynamic users in a real environment, which is composed of user behavior simulation, base station simulation, and terminal simulation. It is applied in specific business scenarios, such as multi-target antenna optimization, compression feedback, and so on. This paper provides detailed descriptions of each emulator and its combination based on this foundation, including the implementation process of the emulator, integration with the platform, experimental results, and other aspects.

Due to shortage of water resources and increasing water demands, the joint operation of multireservoir systems for balancing power generation, ecological protection, and the residential water supply has become a critical issue in hydropower management. However, the numerous constraints and nonlinearity of multiple reservoirs make solving this problem time-consuming. To address this challenge, a deep reinforcement learning approach that incorporates a transformer framework is proposed. The multihead attention mechanism of the encoder effectively extracts information from reservoirs and residential areas, and the multireservoir attention network of the decoder generates suitable operational decisions. The proposed method is applied to Lake Mead and Lake Powell in the Colorado River Basin. The experimental results demonstrate that the transformer-based deep reinforcement learning approach can produce appropriate operational outcomes. Compared to a state-of-the-art method, the operation strategies produced by the proposed approach generate 10.11% more electricity, reduce the amended annual proportional flow deviation by 39.69%, and increase water supply revenue by 4.10%. Consequently, the proposed approach offers an effective method for the multiobjective operation of multihydropower reservoir systems.

Japan on Monday lifted its ban on urban drone flights outside visible range over residential areas to allow aerial parcel deliveries and help address the country's labor shortages amid the greying of the population across the country, particularly in rural areas. Unattended drone flights were previously only allowed over uninhabited areas, such as mountains, rivers and farmlands in so-called level-three operations under the four-tier classification system. Level-four automated drone operations over residential areas will likely begin once operators seeking to provide such services complete government procedures necessary for conducting the flight around March. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.