Cybercriminals using malware and AI tools have stolen over $318 million in cargo shipments nationwide, targeting freight during transit with sophisticated online fraud schemes.

We propose an interpretable machine learning framework to help identify trade data discrepancies that are challenging to detect with traditional methods. Our system analyzes trade data to find a novel inverse price-volume signature, a pattern where reported volumes increase as average unit prices decrease. The model achieves 0.9375 accuracy and was validated by comparing large-scale UN data with detailed firm-level data, confirming that the risk signatures are consistent. This scalable tool provides customs authorities with a transparent, data-driven method to shift from conventional to priority-based inspection protocols, translating complex data into actionable intelligence to support international environmental policies.

Monitoring the internal temperature and humidity of shipping containers is essential to preventing quality degradation during cargo transportation. Sensorless monitoring -- machine learning models that predict the internal conditions of the containers using exogenous factors -- shows promise as an alternative to monitoring using sensors. However, it does not incorporate telemetry information and correct for systematic errors, causing the predictions to differ significantly from the live data and confusing the users. In this paper, we introduce the residual correction method, a general framework for correcting for systematic biases in sensorless models after observing live telemetry data. We call this class of models ``adaptive-sensorless'' monitoring. We train and evaluate adaptive-sensorless models on the 3.48 million data points -- the largest dataset of container sensor readings ever used in academic research -- and show that they produce consistent improvements over the baseline sensorless models. When evaluated on the holdout set of the simulated data, they achieve average mean absolute errors (MAEs) of 2.24 $\sim$ 2.31$^\circ$C (vs 2.43$^\circ$C by sensorless) for temperature and 5.72 $\sim$ 7.09% for relative humidity (vs 7.99% by sensorless) and average root mean-squared errors (RMSEs) of 3.19 $\sim$ 3.26$^\circ$C for temperature (vs 3.38$^\circ$C by sensorless) and 7.70 $\sim$ 9.12% for relative humidity (vs 10.0% by sensorless). Adaptive-sensorless models enable more accurate cargo monitoring, early risk detection, and less dependence on full connectivity in global shipping.

The transnational ivory trade continues to drive the decline of elephant populations across Africa, and trafficking networks remain difficult to disrupt. Tusks seized by law enforcement officials carry forensic information on the traffickers responsible for their export, including DNA evidence and handwritten markings made by traffickers. For 20 years, analyses of tusk DNA have identified where elephants were poached and established connections among shipments of ivory. While the links established using genetic evidence are extremely conclusive, genetic data is expensive and sometimes impossible to obtain. But though handwritten markings are easy to photograph, they are rarely documented or analyzed. Here, we present an AI-driven pipeline for extracting and analyzing handwritten markings on seized elephant tusks, offering a novel, scalable, and low-cost source of forensic evidence. Having collected 6,085 photographs from eight large seizures of ivory over a 6-year period (2014-2019), we used an object detection model to extract over 17,000 individual markings, which were then labeled and described using state-of-the-art AI tools. We identified 184 recurring "signature markings" that connect the tusks on which they appear. 20 signature markings were observed in multiple seizures, establishing forensic links between these seizures through traffickers involved in both shipments. This work complements other investigative techniques by filling in gaps where other data sources are unavailable. The study demonstrates the transformative potential of AI in wildlife forensics and highlights practical steps for integrating handwriting analysis into efforts to disrupt organized wildlife crime.

Only four bottles of Asahi Super Dry beer are left on the shelves of Ben Thai, a cosy restaurant in the Tokyo suburb of Sengawacho. Its owner, Sakaolath Sugizaki, expects to get a few more soon, but she says her supplier is keeping the bulk of its stock for bigger customers. That's because Asahi, the maker of Japan's best-selling beer, was forced to halt production at most of its 30 factories in the country at the end of last month after being hit by a cyber-attack. While all of its facilities in Japan - including six breweries - have now partially reopened, its computer systems are still down. That means it has to process orders and shipments manually - using pen, paper and fax machines - resulting in much fewer shipments than before the attack.

Technology Internet Japan's favorite beer is in peril Asahi Super Dry's manufacturer is suffering from a major cyberattack. Breakthroughs, discoveries, and DIY tips sent every weekday. Japan is facing a serious beer crisis. The emergency began on Monday, September 29 when the makers of the country's most popular brew Asahi Super Dry announced it had suffered a massive cyberattack resulting in a nationwide "system failure." The immediate fallout included a temporary shutdown of nearly all of Asahi Group's 30 domestic breweries, as well a pause in ordering and shipping across Japan.

Authorities in the United States have charged two Chinese citizens with shipping tens of millions of dollars' worth of advanced Nvidia chips to China in breach of export controls. Chuan Geng and Shiwei Yang are alleged to have "knowingly and willfully" exported the graphic processing units (GPUs) used to power artificial intelligence without authorisation from October 2022 to July 2025, the US Department of Justice said on Tuesday. Export records indicate that Geng and Yang, both 28, organised at least 21 shipments through their El Monte, California-based company ALX Solutions Inc to companies in Singapore and Malaysia, the Justice Department said. The exports included a December 2024 shipment of Nvidia H100 GPUs – described as the most powerful chip on the market – that was "falsely labelled" and had not obtained the necessary licence from the US Department of Commerce, the Justice Department said. According to prosecutors, ALX Solutions received payments from firms in Hong Kong and China, including a 1m sum from a China-based company in January 2024, rather than the companies that accepted the shipments.

Two Los Angeles County residents face federal charges after they were arrested on suspicion of illegally exporting tens of millions of dollars' worth of artificial intelligence microchips to China, authorities said. Chuan Geng, 28, of Pasadena; and Shiwei Yang, 28, of El Monte, were taken into custody on Saturday for their alleged involvement in the illegal overseas export of processing units used in modern computing and artificial intelligence applications, according to a statement from the U.S. attorney's office for the Eastern District of California. Federal prosecutors said both were Chinese nationals, though Geng is a lawful permanent resident of the U.S. Yang, however, was in the country illegally as she had overstayed her visa, according to authorities. Yaoning'Mike' Sun of Chino Hills is charged with acting as an illegal agent of a foreign power and conspiring to advance China-friendly policies in local government. In a criminal complaint, U.S. Justice Department officials alleged the pair had "knowingly and willingly" undercut federal export regulations to conceal illegal shipments to China for nearly three years.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Two Chinese nationals -- one of them an illegal immigrant -- were arrested for allegedly shipping tens of millions of dollars' worth of sensitive microchips used in artificial intelligence (AI) applications to China, the Justice Department announced Tuesday. The federal criminal complaint charges Chuan Geng, 28, of Pasadena, California, and Shiwei Yang, 28, of El Monte, California, with violating the Export Control Reform Act. Prosecutors said the felony offense carries a statutory maximum penalty of 20 years in prison.

Interaction-Oriented Programming (IOP) is an approach to building a multiagent system by modeling the interactions between its roles via a flexible interaction protocol and implementing agents to realize the interactions of the roles they play in the protocol. In recent years, we have developed an extensive suite of software that enables multiagent system developers to apply IOP. These include tools for efficiently verifying protocols for properties such as liveness and safety and middleware that simplifies the implementation of agents. This paper presents some of that software suite.