Pope Leo XIV called Sunday for an immediate ceasefire in the war involving Iran, marking the pontiff's strongest remarks yet on conflict.

Iranian forces deploy explosive drone boats disguised as fishing vessels in Strait of Hormuz, defense expert Cameron Chell warns, marking new phase of hybrid warfare.

We devise an algorithm to generate sets of propositions that objectively instantiate graphs that support coherence-driven inference. We then benchmark the ability of large language models (LLMs) to reconstruct coherence graphs from (a straightforward transformation of) propositions expressed in natural language, with promising results from a single prompt to models optimized for reasoning. Combining coherence-driven inference with consistency evaluations by neural models may advance the state of the art in machine cognition.

Grading and quality control of Piarom dates, a premium and high-value variety cultivated predominantly in Iran, present significant challenges due to the complexity and variability of defects, as well as the absence of specialized automated systems tailored to this fruit. Traditional manual inspection methods are labor intensive, time consuming, and prone to human error, while existing AI-based sorting solutions are insufficient for addressing the nuanced characteristics of Piarom dates. In this study, we propose an innovative deep learning framework designed specifically for the real-time detection, classification, and grading of Piarom dates. Leveraging a custom dataset comprising over 9,900 high-resolution images annotated across 11 distinct defect categories, our framework integrates state-of-the-art object detection algorithms and Convolutional Neural Networks (CNNs) to achieve high precision in defect identification. Furthermore, we employ advanced segmentation techniques to estimate the area and weight of each date, thereby optimizing the grading process according to industry standards. Experimental results demonstrate that our system significantly outperforms existing methods in terms of accuracy and computational efficiency, making it highly suitable for industrial applications requiring real-time processing. This work not only provides a robust and scalable solution for automating quality control in the Piarom date industry but also contributes to the broader field of AI-driven food inspection technologies, with potential applications across various agricultural products.

Mussel farming is one of the most important aquaculture industries. The main risk to mussel farming is harmful algal blooms (HABs), which pose a risk to human consumption. In Galicia, the Spanish main producer of cultivated mussels, the opening and closing of the production areas is controlled by a monitoring program. In addition to the closures resulting from the presence of toxicity exceeding the legal threshold, in the absence of a confirmatory sampling and the existence of risk factors, precautionary closures may be applied. These decisions are made by experts without the support or formalisation of the experience on which they are based. Therefore, this work proposes a predictive model capable of supporting the application of precautionary closures. Achieving sensitivity, accuracy and kappa index values of 97.34%, 91.83% and 0.75 respectively, the kNN algorithm has provided the best results. This allows the creation of a system capable of helping in complex situations where forecast errors are more common.

You can become utterly dependent on a new glamorous technology, be it cyber-space, artificial intelligence. . . But does it create a potential achilles heel? Artificial intelligence (AI) has become the technical focal point for advancing naval and Department of Defense (DoD) capabilities. Secretary of the Navy Carlos Del Toro listed AI first among his priorities for innovating U.S. naval forces. Chief of Naval Operations Admiral Michael Gilday listed it as his top priority during his Senate confirmation hearing.2

TEHRAN - Iran's Revolutionary Guard shot down a U.S. surveillance drone Thursday in the Strait of Hormuz, marking the first time the Islamic Republic directly attacked the American military amid tensions over Tehran's unraveling nuclear deal with world powers. The two countries disputed the circumstances leading up to an Iranian surface-to-air missile bringing down the U.S. Navy RQ-4A Global Hawk, an unmanned aircraft with a wingspan larger than a Boeing 737 jetliner and costing over $100 million. Iran said the drone "violated" its territorial airspace, while the U.S. called the missile fire "an unprovoked attack" in international airspace over the narrow mouth of the Persian Gulf and President Donald Trump tweeted that "Iran made a very big mistake!" Trump later appeared to play down the incident, telling reporters in the Oval Office that he had a feeling that "a general or somebody" being "loose and stupid" made a mistake in shooting down the drone. The incident immediately heightened the crisis already gripping the wider region, which is rooted in Trump withdrawing the U.S. a year ago from Iran's 2015 nuclear deal and imposing crippling new sanctions on Tehran.

TEHRAN, Iran – Iran's state-run IRNA news agency says the country's Revolutionary Guard has shot down a U.S. drone. The U.S. military declined to immediately comment. IRNA said Thursday the drone was hit when it entered Iranian airspace near the Kouhmobarak district in southern Iran's Hormozgan province. IRNA, citing the paramilitary Revolutionary Guard, identified the drone as an RQ-4 Global Hawk. Bill Urban, a U.S. Central Command spokesman, declined to comment when asked if an American drone was shot down.

TEHRAN - Iran's Revolutionary Guard said Thursday it shot down a U.S. drone amid heightened tensions between Tehran and Washington over its collapsing nuclear deal. The U.S. military declined to immediately comment. The reported shootdown of the RQ-4 Global Hawk comes after the U.S. military previously alleged Iran fired a missile at another drone last week that responded to the attack on two oil tankers near the Gulf of Oman. The U.S. blames Iran for the attack on the ships, which Tehran denies. The attacks come against the backdrop of heightened tensions between the U.S. and Iran following President Donald Trump's decision to withdraw from Tehran's nuclear deal with world powers a year ago. The White House separately said it was aware of reports of a missile strike on Saudi Arabia amid a campaign targeting the kingdom by Yemen's Iranian-allied Houthi rebels.

For a while now the DigitalGlobe GBDX team has been running machine learning-based object detection at a significant, continental scale. Each time we add a new model to GBDX we kick the tires and do some comparisons to discover advantages or disadvantages over existing capabilities. We keep our customer use cases in mind, which typically boil down to "monitoring and change" or "pattern of life" activities. Some things we monitor with the models we have today include detecting changes or activity in a parking lot or port. With that in mind we wanted to do a "state of the union" or "state of the map" about the current state of machine learning on satellite imagery.