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.

Sequential Decision Making under Uncertainty (SDMU) is ubiquitous in many domains such as energy, finance, and supply chains. Some SDMU applications are naturally modeled as Multistage Stochastic Optimization Problems (MSPs), but the resulting optimizations are notoriously challenging from a computational standpoint. Under assumptions of convexity and stage-wise independence of the uncertainty, the resulting optimization can be solved efficiently using Stochastic Dual Dynamic Programming (SDDP). Two-stage Linear Decision Rules (TS-LDRs) have been proposed to solve MSPs without the stage-wise independence assumption. TS-LDRs are computationally tractable, but using a policy that is a linear function of past observations is typically not suitable for non-convex environments arising, for example, in energy systems. This paper introduces a novel approach, Two-Stage General Decision Rules (TS-GDR), to generalize the policy space beyond linear functions, making them suitable for non-convex environments. TS-GDR is a self-supervised learning algorithm that trains the nonlinear decision rules using stochastic gradient descent (SGD); its forward passes solve the policy implementation optimization problems, and the backward passes leverage duality theory to obtain closed-form gradients. The effectiveness of TS-GDR is demonstrated through an instantiation using Deep Recurrent Neural Networks named Two-Stage Deep Decision Rules (TS-DDR). The method inherits the flexibility and computational performance of Deep Learning methodologies to solve SDMU problems generally tackled through large-scale optimization techniques. Applied to the Long-Term Hydrothermal Dispatch (LTHD) problem using actual power system data from Bolivia, the TS-DDR not only enhances solution quality but also significantly reduces computation times by several orders of magnitude.

We are looking for a highly motivated candidate who holds a Master of Science in Geography, Geology, Archaeology or Natural Sciences. You will be required to carry out fieldwork in the Bolivian Amazon and travel there for a period of 2 to 3 months during the dry seasons (July - October) of 2023 and 2024. The field work will involve flying a drone with a LIDAR over approx. Back in Barcelona your work will focus on building a database of the archaeological sites, calculating the volume of each site and analysing their patterns and properties. You are expected to publish at least 3 papers in well-known scientific journals by the end of the 4 yrs position.

A longstanding mystery about how salt dissolves in water has finally been solved, thanks to machine learning. Understanding the complete process of how sodium chloride, or salt, dissolves in water is important for a range of scientific disciplines, from accurate climate models to making batteries.

A newly discovered network of'lost' ancient cities has been discovered in the Amazon, using lidar technology – dubbed'lasers in the sky' – to peer through the tropical forest canopy. The cities, built by the Casarabe communities between 500-1400 AD, are located in the Llanos de Mojos savannah-forest, Bolivia, and have been hidden under the thick tree canopies for centuries. They feature an array of elaborate and intricate structures unlike any previously discovered in the region, including 16ft-high terraces covering 54 acres – the equivalent of 30 football pitches – and 69ft-tall conical pyramids. The international team of researchers from the UK and Germany also found a vast network of reservoirs, causeways and checkpoints, spanning several miles. The discovery challenges the view of Amazonia as a historically'pristine' landscape, the researchers say, showing it was instead home to an early'urbanism' created and managed by indigenous populations for thousands of years.

A tool has been developed to help healthcare professionals identify hospitalised patients most at risk of dying from COVID-19 using artificial intelligence (AI). The algorithm could help doctors to direct critical care resources to those in most immediate need, which the developers of the AI tool say could be especially valuable to resource-limited countries. And with no end in sight for the coronavirus pandemic, with new variants leading to fresh waves of sickness and hospitalisation, the scientists behind the tool say there is a need for generalised tools like this which can be easily rolled out. To develop the tool, scientists used biochemical data from routine blood samples taken from nearly 30,000 patients hospitalised in over 150 hospitals in Spain, the US, Honduras, Bolivia and Argentina between March 2020 and February 2022. Taking blood from so many patients meant the team were able to capture data from people with different immune statuses – vaccinated, unvaccinated and those with natural immunity – and from people infected with every variant of COVID-19.

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.

Often survey analysis collects data to try to identify response patterns leading to groupings of respondents with different characteristics as revealed by answers provided to survey questions. Without additional background information on respondents, it is often very difficult (and many times impossible) to verify the accuracy of groupings resulting from the analysis. This paper examines one such situation in which high school students in low-income neighbourhood schools in Bolivia responded to a standard periodic institutional survey and responses were analysed to better understand respondents' socioeconomic contexts. In this case study, the question to be answered was "can we identify the most impoverished students based on a 22 questions standard survey alone?". With no known dependent variable and an inability to objectively capture the socioeconomic condition of the students being surveyed, the task of coming to a conclusive answer becomes unfeasible as there is no way to validate at least some portion of the students identified as most impoverished.

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.

It's time to get airdropped into Ghost Recon: Wildlands and Bolivia isn't going to welcome you with open arms. While you're getting acclimated to the lush-yet-treacherous provincial landscapes and trying not to get killed by the Santa Blanca cartel or UNIDAD (the local military police), we've got a few tricks for staying alive and navigating the terrain effectively. There are a myriad of transportation options scattered throughout the provinces. Each has its merits and is useful in different scenarios, depending on where you are and what you're trying to accomplish. Regular Vehicles (cars, trucks, civilian SUVs): In the valleys and hills of Itacua, your best bet is usually hopping into whatever vehicle is available at the rebel checkpoints.