Under the One Big Beautiful Bill Act, data center projects in rural areas could be eligible for major tax benefits starting next year. Some hyperscalers do not seem eager to take the free cash. Welcome back to Power Play! Each week, senior writer Molly Taft tackles a topic around this midterm season's biggest issue: data centers. If you've got a question or thought for the column, feel free to shoot Molly an email at [email protected] or reach them securely on Signal at mollytaft.76.

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A sign showing bear deterrent measures is displayed next to a wolf-shaped robotic device outside a convenience store in a rural area of Kiryu, Gunma Prefecture. Five people were hospitalized after a man accidentally set off an anti-bear spray in a post office in Nagoya's city center, reports and officials said, as Japan grapples with a sharp rise in maulings. A foreign national, who was not named, apologized and told police that he discharged the repellant unintentionally, the reports said. "Eight people felt sick, and of those five were sent to hospital. But there was no report of serious injuries or illness," fire department official Ryohei Asano said after the mishap on Wednesday. At least five people have been killed by bears in Japan since April 1 after a record 13 deaths last fiscal year.

RFK Jr.'s Health Department Is Pondering a National Men's Health Initiative At an FDA discussion of testosterone replacement therapy, a top official called for special health centers to address a "men's health crisis." Others called to ease men's access to hormones. The US Department of Health and Human Services is considering launching a federal men's health initiative, a source at the agency tells WIRED. Brian Christine, who will be sworn in on December 12 as assistant secretary for health at HHS and head of the United States Public Health Service Commissioned Corps, called for such an effort Wednesday during a Food and Drug Administration panel on testosterone replacement therapy (TRT) for men. A spokesperson for HHS declined to comment.

Streaming is great, but still has local programming gaps. We may earn revenue from the products available on this page and participate in affiliate programs. Streaming can be difficult in rural areas, but there's likely still lots of free over-the-air HD content you can access with a solid antenna. When dealing with long distances and tricky terrain, you'll want a robust antenna to pull in those sweet signals. We have chosen the Antennas Direct 8-Element Bowtie as our best overall option for its exceptional range, excellent build quality, and easy of installation.

Virgin Media O2 is set to become the first mobile operator to offer UK customers automatic connectivity via satellite in places without mobile signal. O2 Satellite will be an optional service due to launch in the first half of 2026. The firm has not yet revealed how much it will cost, but it will be an additional fee to pay each month. O2 has partnered with Elon Musk's satellite business Starlink to offer the service. Enabled smartphones will automatically switch to satellite coverage in parts of the UK where there is no terrestrial signal available - for example in rural areas.

Despite rapid progress in vision-language and large language models (VLMs and LLMs), their effectiveness for AI-driven educational assessment in real-world, underrepresented classrooms remains largely unexplored. We evaluate state-of-the-art VLMs and LLMs on over 14K handwritten answers from grade-4 classrooms in Indonesia, covering Mathematics and English aligned with the local national curriculum. Unlike prior work on clean digital text, our dataset features naturally curly, diverse handwriting from real classrooms, posing realistic visual and linguistic challenges. Assessment tasks include grading and generating personalized Indonesian feedback guided by rubric-based evaluation. Results show that the VLM struggles with handwriting recognition, causing error propagation in LLM grading, yet LLM feedback remains pedagogically useful despite imperfect visual inputs, revealing limits in personalization and contextual relevance.

The COVID-19 pandemic has presented significant challenges in Nigeria's public health systems since the first case reported on February 27, 2020. This study investigates key factors that contribute to state vulnerability, quantifying them through a composite risk score integrating population density (weight 0.2), poverty (0.4), access to healthcare (0.3), and age risk (0.1), adjusted by normalized case rates per 100,000. States were categorized into low-, medium-, and high-density areas to analyze trends and identify hotspots using geographic information system (GIS) mapping. The findings reveal that high-density urban areas, such as Lagos, accounting for 35.4% of national cases, had the highest risk scores (Lagos: 673.47 vs. national average: 28.16). These results align with global and local studies on the spatial variability of COVID-19 in Nigeria, including international frameworks such as the CDC Social Vulnerability Index. Google Trends data highlight variations in public health awareness, serving as a supplementary analysis to contextualize vulnerability. The risk score provides a prioritization tool for policymakers to allocate testing, vaccines, and healthcare resources to high-risk areas, though data gaps and rural underreporting call for further research. This framework can extend to other infectious diseases, offering lessons for future pandemics in resource-limited settings.

Rural healthcare faces persistent challenges, including inadequate infrastructure, workforce shortages, and socioeconomic disparities that hinder access to essential services. This study investigates the transformative potential of artificial intelligence (AI) in addressing these issues in underserved rural areas. We systematically reviewed 109 studies published between 2019 and 2024 from PubMed, Embase, Web of Science, IEEE Xplore, and Scopus. Articles were screened using PRISMA guidelines and Covidence software. A thematic analysis was conducted to identify key patterns and insights regarding AI implementation in rural healthcare delivery. The findings reveal significant promise for AI applications, such as predictive analytics, telemedicine platforms, and automated diagnostic tools, in improving healthcare accessibility, quality, and efficiency. Among these, advanced AI systems, including Multimodal Foundation Models (MFMs) and Large Language Models (LLMs), offer particularly transformative potential. MFMs integrate diverse data sources, such as imaging, clinical records, and bio signals, to support comprehensive decision-making, while LLMs facilitate clinical documentation, patient triage, translation, and virtual assistance. Together, these technologies can revolutionize rural healthcare by augmenting human capacity, reducing diagnostic delays, and democratizing access to expertise. However, barriers remain, including infrastructural limitations, data quality concerns, and ethical considerations. Addressing these challenges requires interdisciplinary collaboration, investment in digital infrastructure, and the development of regulatory frameworks. This review offers actionable recommendations and highlights areas for future research to ensure equitable and sustainable integration of AI in rural healthcare systems.

Our estimates of rural populations have systematically underestimated the actual number of people living in these regions by at least half, researchers have claimed – with potentially huge impacts on global population levels and planning for public services. However, the findings are disputed by demographers, who say any such underestimates are unlikely to alter national or global head counts. Josias Láng-Ritter and his colleagues at Aalto University, Finland, were working to understand the extent to which dam construction projects caused people to be resettled, but while estimating populations, they kept getting vastly different numbers to official statistics. To investigate, they used data on 307 dam projects in 35 countries, including China, Brazil, Australia and Poland, all completed between 1980 and 2010, taking the number of people reported as resettled in each case as the population in that area prior to displacement. They then cross-checked these numbers against five major population datasets that break down areas into a grid of squares and estimate the number of people living in each square to arrive at totals.