The whale sustained some injuries during the ordeal, but should recover. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. View of the whale after being freed. Note the red wounds from its most recent entanglement near the tail and the deep, but healing wound, near its head from a prior entanglement. Center for Coastal Studies image, taken under NOAA permit 24359.

Models of hybrid dynamical systems are widely used to answer questions about the causes and effects of dynamic events in time. Unfortunately, existing causal reasoning formalisms lack support for queries involving the dynamically triggered, discontinuous interventions that characterize hybrid dynamical systems. This mismatch can lead to ad-hoc and error-prone causal analysis workflows in practice. To bridge the gap between the needs of hybrid systems users and current causal inference capabilities, we develop a rigorous counterfactual semantics by formalizing interventions as transformations to the constraints of hybrid systems. Unlike interventions in a typical structural causal model, however, interventions in hybrid systems can easily render the model ill-posed. Thus, we identify mild conditions under which our interventions maintain solution existence, uniqueness, and measurability by making explicit connections to established hybrid systems theory. To illustrate the utility of our framework, we formalize a number of canonical causal estimands and explore a case study on the probabilities of causation with applications to fishery management. Our work simultaneously expands the modeling possibilities available to causal inference practitioners and begins to unlock decades of causality research for users of hybrid systems.

In a game of scientific telephone, if you find the food, you find the whales--and sound the alarm. North Atlantic right whales sometimes gather at Jeffrey's Ledge, a 62-mile-long underwater ridge about 25 miles off the coast of Portsmouth, New Hampshire. Breakthroughs, discoveries, and DIY tips sent six days a week. It was a cold and windy week last January, when a group of Maine lobstermen couldn't haul in their traps from Jeffrey's Ledge. The reason why surprised everyone.

Technology Engineering Afraid your fish is too fishy? Microneedles can tell when things start getting rancid long before we notice smells. Breakthroughs, discoveries, and DIY tips sent every weekday. A new biosensor made out of needles most commonly seen in dermatology clinics and medspas could make the fresh fish " smell test " seem antiquated. For as long as humans have eaten fish, we've identified rot or spoilage by looking for a handful of physical signs .

Monitoring aquatic species, especially elusive ones like lobsters, presents challenges. This study focuses on Homarus gammarus (European lobster), a key species for fisheries and aquaculture, and leverages non-invasive Passive Acoustic Monitoring (PAM). Understanding lobster habitats, welfare, reproduction, sex, and age is crucial for management and conservation. While bioacoustic emissions have classified various aquatic species using Artificial Intelligence (AI) models, this research specifically uses H. gammarus bioacoustics (buzzing/carapace vibrations) to classify lobsters by age (juvenile/adult) and sex (male/female). The dataset was collected at Johnshaven, Scotland, using hydrophones in concrete tanks. We explored the efficacy of Deep Learning (DL) models (1D-CNN, 1D-DCNN) and six Machine Learning (ML) models (SVM, k-NN, Naive Bayes, Random Forest, XGBoost, MLP). Mel-frequency cepstral coefficients (MFCCs) were used as features. For age classification (adult vs. juvenile), most models achieved over 97% accuracy (Naive Bayes: 91.31%). For sex classification, all models except Naive Bayes surpassed 93.23%. These strong results demonstrate the potential of supervised ML and DL to extract age- and sex-related features from lobster sounds. This research offers a promising non-invasive PAM approach for lobster conservation, detection, and management in aquaculture and fisheries, enabling real-world edge computing applications for underwater species.

Humanity faces numerous problems of common-pool resource appropriation.

A Swedish man discovered the 12th century buried treasure near his summer home. Breakthroughs, discoveries, and DIY tips sent every weekday. It only costs a few dollars to buy a tub of bait worms for fishing, but many people are fine with sourcing them straight from the ground. There's always a chance you may find more in the dirt than wriggling invertebrates. Take a recent example near Stockholm, Sweden: According to county officials last month, an unnamed fisherman scrounging for worms at his summer house discovered a corroded copper cauldron containing around 13 pounds of treasure from the Middle Ages.

The North Indian Ocean, including the Arabian Sea and the Bay of Bengal, represents a vital source of livelihood for coastal communities, yet fishermen often face uncertainty in locating productive fishing grounds. To address this challenge, we present an AI-assisted framework for predicting Potential Fishing Zones (PFZs) using oceanographic parameters such as sea surface temperature and chlorophyll concentration. The approach is designed to enhance the accuracy of PFZ identification and provide region-specific insights for sustainable fishing practices. Preliminary results indicate that the framework can support fishermen by reducing search time, lowering fuel consumption, and promoting efficient resource utilization.