Accurate and timely prediction of heavy rainfall events is crucial for effective flood risk management and disaster preparedness. By monitoring, analysing, and evaluating rainfall data at a local level, it is not only possible to take effective actions to prevent any severe climate variation but also to improve the planning of surface and underground hydrological resources. However, developing countries often lack the weather stations to collect data continuously due to the high cost of installation and maintenance. In light of this, the contribution of the present paper is twofold: first, we propose a low-cost IoT system for automatic recording, monitoring, and prediction of rainfall in rural regions. Second, we propose a novel approach to regional heavy rainfall prediction by implementing graph neural networks (GNNs), which are particularly well-suited for capturing the complex spatial dependencies inherent in rainfall patterns. The proposed approach was tested using a historical dataset spanning 72 months, with daily measurements, and experimental results demonstrated the effectiveness of the proposed method in predicting heavy rainfall events, making this approach particularly attractive for regions with limited resources or where traditional weather radar or station coverage is sparse.

Large language models (LLMs) have demonstrated exceptional performance across a wide variety of domains. Nonetheless, generalist LLMs continue to fall short in reasoning tasks necessitating specialized knowledge. Prior investigations into specialized LLMs focused on domain-specific training, which entails substantial efforts in domain data acquisition and model parameter fine-tuning. To address these challenges, this paper proposes the Way-to-Specialist (WTS) framework, which synergizes retrieval-augmented generation with knowledge graphs (KGs) to enhance the specialized capability of LLMs in the absence of specialized training. In distinction to existing paradigms that merely utilize external knowledge from general KGs or static domain KGs to prompt LLM for enhanced domain-specific reasoning, WTS proposes an innovative "LLM$\circlearrowright$KG" paradigm, which achieves bidirectional enhancement between specialized LLM and domain knowledge graph (DKG). The proposed paradigm encompasses two closely coupled components: the DKG-Augmented LLM and the LLM-Assisted DKG Evolution. The former retrieves question-relevant domain knowledge from DKG and uses it to prompt LLM to enhance the reasoning capability for domain-specific tasks; the latter leverages LLM to generate new domain knowledge from processed tasks and use it to evolve DKG. WTS closes the loop between DKG-Augmented LLM and LLM-Assisted DKG Evolution, enabling continuous improvement in the domain specialization as it progressively answers and learns from domain-specific questions. We validate the performance of WTS on 6 datasets spanning 5 domains. The experimental results show that WTS surpasses the previous SOTA in 4 specialized domains and achieves a maximum performance improvement of 11.3%.

Natural Language Processing (NLP) has advanced to the point where it can allow humans to interact with machines in non-technical terms. This opens the door to deriving value very quickly from the most complex enterprise data management systems. Natural language processing (NLP) is a branch of AI focused on enabling humans to communicate with computers in human languages vs. computer languages. When humans speak to each other, the conversations are flexible and fluid. One typo in code syntax, and the entire function totally breaks down.

Due to a lack of reliable data collection systems, traffic fatalities and injuries are often under-reported in developing countries. Recent developments in surrogate road safety methods and video analytics tools offer an alternative approach that can be both lower cost and more time efficient when crash data is incomplete or missing. However, very few studies investigating pedestrian road safety in developing countries using these approaches exist. This research uses an automated video analytics tool to develop and analyze surrogate traffic safety measures and to evaluate the effectiveness of temporary low-cost countermeasures at selected pedestrian crossings at risky intersections in the city of Cochabamba, Bolivia. Specialized computer vision software is used to process hundreds of hours of video data and generate data on road users' speed and trajectories.

This article was written by Kate Crawford & Vladan Joler. Below is an extract, featuring the first three sections of this long article (21 sections total.) Link to the full article is provided at the bottom. A cylinder sits in a room. It is impassive, smooth, simple and small.

Romeo, an 11-year-old frog from Cochabamba City, Bolivia, has been given his own online dating profile in a bid to save his species. The Sehuencas water frog, who is the last known individual of his species, has not had a partner for more than 10 years. Conservation groups have teamed up with Match.com to give Romeo a platform and raise awareness for his story as well as funds for an expedition to find him a mate. If all else fails, one of the researchers on the project wont rule out cloning as a means of preserving this amphibian species which, like many others, is threatened by climate change, habitat loss and other environmental and ecological issues. Romeo, an 11-year-old frog from Cochabamba City, Bolivia, has been given his own online dating profile in a bid to save his species.

I have been writing about video games full time for nearly seven years now, and I don't know if I've ever experienced a month like this past one before. Not to complain about playing and reviewing games for a living, but wow this has been intense. I started with 30 hours of Horizon Zero Dawn in a week in order to write that review. Then, my Switch arrived, and I sunk about 80 hours in Zelda: Breath of the Wild, which I wasn't reviewing, but wanted to play and write about constantly all the same since it's arguably one of the best games ever made. And I only put that game down when my review copy of Mass Effect: Andromeda showed up, and to hit that embargo, I put in 60 hours in six days.

We can roughly divide the year into two halves: September through February is the "Release" bit, and March through August the "Hype" bit. Sure, there are exceptions--games get released all year long at this point. The Game Developer's Conference, which took place this week in San Francisco and where we start to see news about the year's most-anticipated games. Another solid Humble Bundle this week, this time focused on the Arma series. The tiers are a bit complicated but basically $1 gets you the original Arma, beating the average gets you Arma 2, and paying $15 or more gets you Arma 3. If you're looking for a hellishly complicated but ultra-rewarding military sim, Arma's a damn good choice--or if you just want to play the original DayZ, before it fell into Early Access hell.

Bolivia may be one of the cocaine capitals of the world – but don't you dare call it a narco state. The Bolivian government complained to France after a French video game publisher, Ubisoft, developed a game that highly offended the South American nation. The popular game, "Tom Clancy's Ghost Recon Wildlands," revolves around a Mexican drug cartel that controls Bolivia and has turned the country into a violent narco-state. The game is set to be officially launched next week, but the game's beta version has already been downloaded by 6.8 million users. Bolivian Interior Minister Carlos Romero said the Andean nation delivered a letter to the French ambassador earlier this week and asked French officials to intervene.