In regions like Punjab, India, where groundwater levels are plummeting at 41.6 cm/year, adopting water-saving rice farming practices is critical. Direct-Seeded Rice (DSR) and Alternate Wetting and Drying (A WD) can cut irrigation water use by 20-40% without hurting yields, yet lack of spatial data on adoption impedes effective adaptation policy and climate action. We present a machine learning framework to bridge this data gap by monitoring sustainable rice farming at scale. In collaboration with agronomy experts and a large-scale farmer training program, we obtained ground-truth data from 1,400 fields across Punjab. Leveraging this partnership, we developed a novel dimensional classification approach that decouples sowing and irrigation practices, achieving F1 scores of 0.8 and 0.74 respectively, solely employing Sentinel-1 satellite imagery. Explainability analysis reveals that DSR classification is robust while A WD classification depends primarily on planting schedule differences, as Sentinel-1's 12-day revisit frequency cannot capture the higher frequency irrigation cycles characteristic of A WD practices. Applying this model across 3 million fields reveals spatial heterogeneity in adoption at the state level, highlighting gaps and opportunities for policy targeting. Our district-level adoption rates correlate well with government estimates (Spearman's ρ=0.69 and Rank Biased Overlap=0.77). This study provides policymakers and sustainability programs a powerful tool to track practice adoption, inform targeted interventions, and drive data-driven policies for water conservation and climate mitigation at regional scale.

Air pollution is a common and serious problem nowadays and it cannot be ignored as it has harmful impacts on human health. To address this issue proactively, people should be aware of their surroundings, which means the environment where they survive. With this motive, this research has predicted the AQI based on different air pollutant concentrations in the atmosphere. The dataset used for this research has been taken from the official website of CPCB. The dataset has the air pollutant concentration from 22 different monitoring stations in different cities of Delhi, Haryana, and Punjab. This data is checked for null values and outliers. But, the most important thing to note is the correct understanding and imputation of such values rather than ignoring or doing wrong imputation. The time series data has been used in this research which is tested for stationarity using The Dickey-Fuller test. Further different ML models like CatBoost, XGBoost, Random Forest, SVM regressor, time series model SARIMAX, and deep learning model LSTM have been used to predict AQI. For the performance evaluation of different models, I used MSE, RMSE, MAE, and R2. It is observed that Random Forest performed better as compared to other models. NTRODUCTION Putting all the life threats aside, air pollution is the deadliest health threat to all species. In other words, it can be called a silent killer. It is estimated by WHO, that around 7 million people die every year this is because of the presence of deadly fine particles in the air, which lead to diseases such as stroke, heart disease, lung cancer, chronic obstructive pulmonary diseases, and respiratory infections, including pneumonia.

London, Dec 9, 2022- UK Prime Minister Rishi Sunak is set to announce a collaboration between Britain, Italy and Japan to develop a new fighter jet that uses artificial intelligence (AI). According to Sunak, the joint venture aims to create thousands of UK jobs and strengthen security ties, reports the BBC. The nations will develop a next generation fighter, due to enter service in the mid-2030s, that will eventually replace the Typhoon jet. It is hoped the new Tempest jet will carry the latest weapons. Work on developing it is already under way, with the aim to create a combat aircraft that will provide speed stealth, use advanced sensors and even AI to assist the human pilot when they are overwhelmed, or under extreme stress, the BBC reported. It could also be flown without a pilot's input if required and could be able to fire hypersonic missiles.

The long-running BBC series Doctor Who recently completed its first season with Jodie Whittaker as the titular Doctor. Writer Sara Lynn Michener says that having a female Doctor came as a welcome change of pace. "This formula of always having female companions, and always having male Doctors, it just made me think of Doctor Who in a certain way that wasn't very flattering," Michener says in Episode 343 of the Geek's Guide to the Galaxy podcast. "It felt less real, because if this alien does in fact have the ability to regenerate in all of these bodies, why are we still seeing this very standard, very heteronormative pairing constantly?" Science fiction author Rajan Khanna also enjoyed Whittaker's performance, and found that this season of Doctor Who was the first one he was able to watch with his girlfriend. "She's tried to get into it previously, and just bounced off of it," he says. "And this season I was like, 'I'm going to watch Doctor Who.