India's communists once ruled millions. For the first time since 1957, India no longer has a single communist-led state government. The defeat of the Communist Party of India (Marxist)-led Left Democratic Front (LDF) in Kerala this month, after a decade in power, marked the end - at least for now - of one of the world's most enduring experiments in democratic communism. At their peak, India's communist parties ruled states stretching from West Bengal to Kerala and Tripura. They impacted the lives of more than 100 million people through trade unions, peasant organisations, student wings and disciplined cadre networks.

Preserving linguistic diversity is necessary as every language offers a distinct perspective on the world. There have been numerous global initiatives to preserve endangered languages through documentation. This paper is a part of a project which aims to develop a trilingual (Toto-Bangla-English) language learning application to digitally archive and promote the endangered Toto language of West Bengal, India. This application, designed for both native Toto speakers and non-native learners, aims to revitalize the language by ensuring accessibility and usability through Unicode script integration and a structured language corpus. The research includes detailed linguistic documentation collected via fieldwork, followed by the creation of a morpheme-tagged, trilingual corpus used to train a Small Language Model (SLM) and a Transformer-based translation engine. The analysis covers inflectional morphology such as person-number-gender agreement, tense-aspect-mood distinctions, and case marking, alongside derivational strategies that reflect word-class changes. Script standardization and digital literacy tools were also developed to enhance script usage. The study offers a sustainable model for preserving endangered languages by incorporating traditional linguistic methodology with AI. This bridge between linguistic research with technological innovation highlights the value of interdisciplinary collaboration for community-based language revitalization.

Air pollution kills 7 million people annually. The brick kiln sector significantly contributes to economic development but also accounts for 8-14\% of air pollution in India. Policymakers have implemented compliance measures to regulate brick kilns. Emission inventories are critical for air quality modeling and source apportionment studies. However, the largely unorganized nature of the brick kiln sector necessitates labor-intensive survey efforts for monitoring. Recent efforts by air quality researchers have relied on manual annotation of brick kilns using satellite imagery to build emission inventories, but this approach lacks scalability. Machine-learning-based object detection methods have shown promise for detecting brick kilns; however, previous studies often rely on costly high-resolution imagery and fail to integrate with governmental policies. In this work, we developed a scalable machine-learning pipeline that detected and classified 30638 brick kilns across five states in the Indo-Gangetic Plain using free, moderate-resolution satellite imagery from Planet Labs. Our detections have a high correlation with on-ground surveys. We performed automated compliance analysis based on government policies. In the Delhi airshed, stricter policy enforcement has led to the adoption of efficient brick kiln technologies. This study highlights the need for inclusive policies that balance environmental sustainability with the livelihoods of workers.

This study explored the application of portable X-ray fluorescence (PXRF) spectrometry and soil image analysis to rapidly assess soil fertility, focusing on critical parameters such as available B, organic carbon (OC), available Mn, available S, and the sulfur availability index (SAI). Analyzing 1,133 soil samples from various agro-climatic zones in Eastern India, the research combined color and texture features from microscopic soil images, PXRF data, and auxiliary soil variables (AVs) using a Random Forest model. Results indicated that integrating image features (IFs) with auxiliary variables (AVs) significantly enhanced prediction accuracy for available B (R^2 = 0.80) and OC (R^2 = 0.88). A data fusion approach, incorporating IFs, AVs, and PXRF data, further improved predictions for available Mn and SAI with R^2 values of 0.72 and 0.70, respectively. The study demonstrated how these integrated technologies have the potential to provide quick and affordable options for soil testing, opening up access to more sophisticated prediction models and a better comprehension of the fertility and health of the soil. Future research should focus on the application of deep learning models on a larger dataset of soil images, developed using soils from a broader range of agro-climatic zones under field condition.

A group of researchers from IIT Kharagpur in West Bengal has successfully predicted the presence of arsenic in groundwater and its adverse effect on human health in affected areas using Artificial Intelligence (AI) algorithms on environmental, geological and human usage parameters. They also successfully managed to delineate the high and low arsenic zones across the Ganges River delta using AI and quantify the number of people exposed. Madhumita Chakraborty, the lead author of the paper, said, "Our AI models predict the occurrence of high arsenic in groundwater across more than half of the Ganges River delta, covering more than 25% area in each of the 19 out of 25 administrative zones in West Bengal. A total of 30.3 million people are estimated to be exposed to severely high As-hazard within the Ganges River delta." The AI findings will be a boon in the Eastern states where arsenic has been a concern, especially along the banks of the Ganga for almost two decades, putting millions of people at severe health risk.