Food security is a complex, multidimensional concept challenging to measure comprehensively. Effective anticipation, monitoring, and mitigation of food crises require timely and comprehensive global data. This paper introduces the Harmonized Food Insecurity Dataset (HFID), an open-source resource consolidating four key data sources: the Integrated Food Security Phase Classification (IPC)/Cadre Harmonis\'e (CH) phases, the Famine Early Warning Systems Network (FEWS NET) IPC-compatible phases, and the World Food Program's (WFP) Food Consumption Score (FCS) and reduced Coping Strategy Index (rCSI). Updated monthly and using a common reference system for administrative units, the HFID offers extensive spatial and temporal coverage. It serves as a vital tool for food security experts and humanitarian agencies, providing a unified resource for analyzing food security conditions and highlighting global data disparities. The scientific community can also leverage the HFID to develop data-driven predictive models, enhancing the capacity to forecast and prevent future food crises.

Humanitarian organizations can enhance their effectiveness by analyzing data to discover trends, gather aggregated insights, manage their security risks, support decision-making, and inform advocacy and funding proposals. However, data about violent incidents with direct impact and relevance for humanitarian aid operations is not readily available. An automatic data collection and NLP-backed classification framework aligned with humanitarian perspectives can help bridge this gap. In this paper, we present HumVI - a dataset comprising news articles in three languages (English, French, Arabic) containing instances of different types of violent incidents categorized by the humanitarian sector they impact, e.g., aid security, education, food security, health, and protection. Reliable labels were obtained for the dataset by partnering with a data-backed humanitarian organization, Insecurity Insight. We provide multiple benchmarks for the dataset, employing various deep learning architectures and techniques, including data augmentation and mask loss, to address different task-related challenges, e.g., domain expansion. The dataset is publicly available at https://github.com/dataminr-ai/humvi-dataset.

Food security (FS) is a complex and multifaceted problem, influenced by several factors such as weather events, economic shocks, and natural disasters. Understanding the dynamics of food security is crucial for effective policymaking and humanitarian efforts. While conflicts and violent events increasingly stand out as key drivers of food crises[1], the depth of their impact remains largely underexplored. Examining the quantitative aspects of this impact is essential for developing more targeted interventions and strategies to address the complex interplay between conflict and food security. Existing research tends to be qualitative in nature (Kemmerling et al.2022; Brown et al. 2020; Brown et al. 2021), leaving a significant gap in understanding the quantitative aspects of how conflicts impact FS levels. By delving into quantitative analyses, we can not only enhance our comprehension of the magnitude of the problem but also pave the way for evidence-based decision-making in efforts to alleviate food insecurity in conflict-affected regions. Regarding the qualitative study of conflicts and FS, Kemmerling et al.(2022)[2] provided a comprehensive explanation on how violence and armed conflicts impact FS through destruction, displacement, financing of conflicts and food being used as a weapon. The authors call for better conflict data collection, and an increase in focus on the study of conflicts early warnings.

Russia's overnight attacks on Ukraine also left several people wounded in the southern city of Zaporizhzhia. Meanwhile, Ukrainian drones targeted a military airfield in the Maikop region of southern Russia. Local officials evacuated 40 people from a nearby village. Russia's missile strike on the Odesa region hit a Panamanian-registered ship on Wednesday night, Oleh Kiper said - two days after a Palau-flagged ship was attacked, leaving one dead on board. Another ship, which was said to be carrying 6,000 tonnes of corn, was attacked on Sunday.

Hunger crises are critical global issues affecting millions, particularly in low-income and developing countries. This research investigates how machine learning can be utilized to predict and inform decisions regarding famine and hunger crises. By leveraging a diverse set of variables (natural, economic, and conflict-related), three machine learning models (Linear Regression, XGBoost, and RandomForestRegressor) were employed to predict food consumption scores, a key indicator of household nutrition. The RandomForestRegressor emerged as the most accurate model, with an average prediction error of 10.6%, though accuracy varied significantly across countries, ranging from 2% to over 30%. Notably, economic indicators were consistently the most significant predictors of average household nutrition, while no single feature dominated across all regions, underscoring the necessity for comprehensive data collection and tailored, country-specific models. These findings highlight the potential of machine learning, particularly Random Forests, to enhance famine prediction, suggesting that continued research and improved data gathering are essential for more effective global hunger forecasting.

The increasing utilization of emerging technologies in the Food & Agriculture (FA) sector has heightened the need for security to minimize cyber risks. Considering this aspect, this manuscript reviews disclosed and documented cybersecurity incidents in the FA sector. For this purpose, thirty cybersecurity incidents were identified, which took place between July 2011 and April 2023. The details of these incidents are reported from multiple sources such as: the private industry and flash notifications generated by the Federal Bureau of Investigation (FBI), internal reports from the affected organizations, and available media sources. Considering the available information, a brief description of the security threat, ransom amount, and impact on the organization are discussed for each incident. This review reports an increased frequency of cybersecurity threats to the FA sector. To minimize these cyber risks, popular cybersecurity frameworks and recent agriculture-specific cybersecurity solutions are also discussed. Further, the need for AI assurance in the FA sector is explained, and the Farmer-Centered AI (FCAI) framework is proposed. The main aim of the FCAI framework is to support farmers in decision-making for agricultural production, by incorporating AI assurance. Lastly, the effects of the reported cyber incidents on other critical infrastructures, food security, and the economy are noted, along with specifying the open issues for future development.

Summary: NHANES, the National Health and Nutrition Examination Survey, is a program of studies led by the Centers for Disease Control and Prevention (CDC) designed to assess the health and nutritional status of adults and children in the United States (U.S.). NHANES data is frequently used by biostatisticians and clinical scientists to study health trends across the U.S., but every analysis requires extensive data management and cleaning before use and this repetitive data engineering collectively costs valuable research time and decreases the reproducibility of analyses. Here, we introduce NHANES-GCP, a Cloud Development Kit for Terraform (CDKTF) Infrastructure-as-Code (IaC) and Data Build Tool (dbt) resources built on the Google Cloud Platform (GCP) that automates the data engineering and management aspects of working with NHANES data. With current GCP pricing, NHANES-GCP costs less than $2 to run and less than $15/yr of ongoing costs for hosting the NHANES data, all while providing researchers with clean data tables that can readily be integrated for large-scale analyses. We provide examples of leveraging BigQuery ML to carry out the process of selecting data, integrating data, training machine learning and statistical models, and generating results all from a single SQL-like query. NHANES-GCP is designed to enhance the reproducibility of analyses and create a well-engineered NHANES data resource for statistics, machine learning, and fine-tuning Large Language Models (LLMs). Availability and implementation" NHANES-GCP is available at https://github.com/In-Vivo-Group/NHANES-GCP

Be it the news or the dwindling number of creatures hitting your windscreens, it will not have evaded you that the insect world in bad shape. In the last three decades, the global biomass of flying insects has shrunk by 75%. Among the trend's most notables victims is the world's most important pollinator, the honeybee. In the United States, 48% of honeybee colonies died in 2023 alone, making it the second deadliest year on record. This significant loss is due in part to colony collapse disorder (CCD), the sudden disappearance of bees.

Early warning systems are an essential tool for effective humanitarian action. Advance warnings on impending disasters facilitate timely and targeted response which help save lives, livelihoods, and scarce financial resources. In this work we present a new quantitative methodology to forecast levels of food consumption for 60 consecutive days, at the sub-national level, in four countries: Mali, Nigeria, Syria, and Yemen. The methodology is built on publicly available data from the World Food Programme's integrated global hunger monitoring system which collects, processes, and displays daily updates on key food security metrics, conflict, weather events, and other drivers of food insecurity across 90 countries (https://hungermap.wfp.org/). In this study, we assessed the performance of various models including ARIMA, XGBoost, LSTMs, CNNs, and Reservoir Computing (RC), by comparing their Root Mean Squared Error (RMSE) metrics. This comprehensive analysis spanned classical statistical, machine learning, and deep learning approaches. Our findings highlight Reservoir Computing as a particularly well-suited model in the field of food security given both its notable resistance to over-fitting on limited data samples and its efficient training capabilities. The methodology we introduce establishes the groundwork for a global, data-driven early warning system designed to anticipate and detect food insecurity.

Hydropower plants are crucial for stable renewable energy and serve as vital water sources for sustainable agriculture. However, it is essential to assess the current water management practices associated with hydropower plant management software. A key concern is the potential conflict between electricity generation and agricultural water needs. Prioritising water for electricity generation can reduce irrigation availability in agriculture during crucial periods like droughts, impacting crop yields and regional food security. Coordination between electricity and agricultural water allocation is necessary to ensure optimal and environmentally sound practices. Neural networks have become valuable tools for hydropower plant management, but their black-box nature raises concerns about transparency in decision making. Additionally, current approaches often do not take advantage of their potential to create a system that effectively balances water allocation. This work is a call for attention and highlights the potential risks of deploying neural network-based hydropower plant management software without proper scrutiny and control. To address these concerns, we propose the adoption of the Agriculture Conscious Hydropower Plant Management framework, aiming to maximise electricity production while prioritising stable irrigation for agriculture. We also advocate reevaluating government-imposed minimum water guidelines for irrigation to ensure flexibility and effective water allocation. Additionally, we suggest a set of regulatory measures to promote model transparency and robustness, certifying software that makes conscious and intelligent water allocation decisions, ultimately safeguarding agriculture from undue strain during droughts.