In estimations of ocean heat content – important when assessing and predicting the effects of climate change – calculations have often presented the rate of warming as a gradual rise from the mid 20th century to today. However, new research from UC Santa Barbara scientists Timothy DeVries and Aaron Bagnell could overturn that assumption, suggesting the ocean maintained a relatively steady temperature throughout most of the 20th century, before embarking on a steep rise. The newly discovered dynamics may have significant implications for what we might expect in the future. "There wasn't an onset of an imbalance until about 1990, which is later than most estimates," said DeVries, an associate professor in the Department of Geography, and a co-author on a paper that appears in the journal Nature Communications. According to the study, the period from 1950 to1990 saw temperature fluctuations in the water column but no net warming.

To do that, the nonprofit is implementing new technology like better video review platforms, better cameras on boats, and increased artificial intelligence, which CEO Mark Hager said is the most exciting. New England Marine Monitoring, in partnership with the Gulf of Maine Research Institute and Vesper, is developing artificial intelligence for fishermen. The goal is to make commercial fishing both economically and ecologically better. Typically, there are human observers on a boat to be sure the fishermen are following federal guidelines, but this technology could change that. "The idea is to ultimately shift from having at-sea human observers," Blaine Grimes of the Gulf of Maine Research Institute said.

Microorganisms are widely distributed in the human daily living environment. They play an essential role in environmental pollution control, disease prevention and treatment, and food and drug production. The identification, counting, and detection are the basic steps for making full use of different microorganisms. However, the conventional analysis methods are expensive, laborious, and time-consuming. To overcome these limitations, artificial neural networks are applied for microorganism image analysis. We conduct this review to understand the development process of microorganism image analysis based on artificial neural networks. In this review, the background and motivation are introduced first. Then, the development of artificial neural networks and representative networks are introduced. After that, the papers related to microorganism image analysis based on classical and deep neural networks are reviewed from the perspectives of different tasks. In the end, the methodology analysis and potential direction are discussed.

Human-wildlife conflict—defined here as direct interactions between humans and wildlife with adverse outcomes—costs the global economy billions of dollars annually, threatens human lives and livelihoods, and is a leading cause of biodiversity loss ([ 1 ][1]). These clashes largely stem from the co-occurrence of humans and wildlife seeking limited resources in shared landscapes and often has unforeseen consequences. For example, large carnivore species like leopards may prey upon livestock and disrupt human livelihoods, leading to retaliatory killings that can drive wildlife decline, zoonotic disease outbreaks, and child labor practices ([ 2 ][2]). As dire as these conflicts have been, climate change is intensifying human-wildlife conflict by exacerbating resource scarcity and forcing people and wildlife to share increasingly crowded spaces. Consequently, human-wildlife conflict is rising in frequency and severity, but the complex connections among climate dynamics, ecological dynamics, and social dynamics contributing to the heightened conflict have yet to be fully appreciated. ![Figure][3] Warming temperatures have driven animals to human-dominated areas in search of food. Increased attacks on livestock can spur retaliatory killing of predators. A sheep corral in the Himalayas is covered with wire to protect against attacks from snow leopards. PHOTO: NICK GARBUTT/MINDEN PICTURES Both extreme climate events and directional climate change have the potential to alter the dynamics of human-wildlife conflict. Acute climate events can cause rapid changes in resource availability that drive strong behavioral and spatial responses in animals and people, leading to increased co-occurrence and competition. In terrestrial systems, droughts in particular have intensified some of the most visible conflicts. For example, from 1986 to 1988, a severe drought in India brought about by an extreme El Niño led to a sharp decline in vegetation productivity; loss of food drove elephants to new human-dominated areas, which led to rapid increases in crop damage and fatal attacks on people ([ 3 ][4]). The same drought event in India saw a marked increase in livestock losses to lions, and human fatalities from lion attacks rose by more than 600% in one region to 6.7 deaths per year following the drought ([ 3 ][4]). More recently in 2018, a prolonged drought in Botswana saw some of the highest incidences of livestock depredations by large carnivores on record, compounding drought-induced food and economic insecurity in agricultural and pastoral communities ([ 4 ][5]). Similar connections between climate events and conflicts are occurring in marine systems. For instance, anomalously warm water temperatures off the South African coast drove changes in prey availability that displaced great white sharks into areas of high human use; the increase in spatial overlap between people and sharks led to a nearly fourfold increase in shark attacks within a single year ([ 5 ][6]). A similar increase in spatial overlap that resulted in heightened conflict occurred in 2014 to 2016 off the US West Coast, when an intense marine heat wave drove changes in both large-whale distributions and fisheries management, leading to an unprecedented number of whale entanglements in fishing gear ([ 6 ][7]). Not only did these entanglements cause high rates of whale mortality, but subsequent management restrictions have threatened millions of dollars in lost fishery revenue. Although extreme climate events often create dramatic conflicts, long-term warming is also producing conflicts with interconnected consequences for people and wildlife. In a notable example, over a 30-year period in Canada's Hudson Bay, human–polar bear conflicts involving property damage, life-threatening encounters, or bear killings have more than tripled as sea ice has declined and polar bears have spent more time on land ([ 7 ][8]). In the Himalayas, warming-induced vegetation changes at high elevations have driven the bharal or blue sheep to lower elevations, where they forage on crops, which affects the livelihoods of local subsistence agricultural producers. Simultaneously, the redistribution of bharal has also drawn their primary predator, snow leopards, to lower elevations, leading to increased livestock depredation and retaliatory killing of leopards ([ 8 ][9]). In other examples, crop foraging ([ 9 ][10]), livestock depredation ([ 10 ][11]) or competition ([ 11 ][12]), and human-wildlife encounters ([ 12 ][13]) are inversely correlated with interannual rainfall as a result of reduced food and water availability, and declining rainfall trends in parts of the globe continue to create more frequent and intense conflicts ([ 13 ][14]). Even as climate change restricts resource availability in many contexts, climate-driven expansion of the human footprint further forces people and animals to share spaces and can create new conflicts—for example, agricultural expansion into previously unproductive or inaccessible areas is significantly associated with rises in human-wildlife conflict ([ 9 ][10]). By investigating the interrelated consequences of climate change on wildlife and human populations, we can better anticipate undesired outcomes and identify how human interventions can mitigate cascading ecological and social dynamics. Climate impacts on human-wildlife conflict do not act in isolation—among other factors, socioeconomic drivers such as land-use change and demographic processes such as rising human populations or changes in predator and prey populations play major roles in determining the frequency, scale, and distribution of conflicts ([ 1 ][1]). Thus, illuminating and ultimately addressing the interconnections between climate change and human-wildlife conflict requires a coupled socioecological systems approach, drawing from fields as diverse as ecology, global change biology, human demography, political science, public policy, history, and economics. Although the impact of climate change on human-wildlife conflict has arguably received relatively little research attention, governmental bodies are increasingly recognizing this phenomenon and developing forward-looking policies to explicitly incorporate climate into the management of certain conflicts ([ 3 ][4], [ 4 ][5]). For example, the state of California in the US recently implemented a Risk Assessment and Mitigation Program that assimilates climatic, oceanographic, biological, and economic indices to inform dynamic fisheries management to reduce the risk of whale entanglements ([ 6 ][7]). Knowledge of climate impacts on human-wildlife conflict can also aid long-term planning efforts and public outreach. For instance, livestock compensation programs, one of the most widely implemented tools to mitigate human-carnivore conflict, could plan funding allocations to anticipate higher spending in years with anomalous climate conditions. Furthermore, given early warning from climate predictions or emerging efforts to predict human-wildlife conflicts using artificial intelligence ([ 14 ][15]), governments or nongovernmental organizations can educate and warn the public about possible increased interactions with wildlife ([ 12 ][13]). As climate change continues to drive both increased climate variability and directional change ([ 15 ][16]), climate-driven human-wildlife conflict can be expected to be a recurring challenge. To protect wildlife and humans alike, it is vital that a diverse body of research and institutions considers the role of a changing climate in shaping the complex socioecological dynamics of conflict. 1. [↵][17]1. P. J. Nyhus , Annu. Rev. Environ. Resour. 41, 143 (2016). [OpenUrl][18] 2. [↵][19]1. J. Terborgh, 2. J. A. Estes 1. J. S. Brashares, 2. L. R. Prugh, 3. C. J. Stoner, 4. C. W. Epps , in Trophic Cascades, J. Terborgh, J. A. Estes, Eds. (Island Press, 2010), pp. 221–240. 3. [↵][20]1. J. R. Bhatt, 2. A. Das, 3. K. Shanker , Eds., Biodiversity and Climate Change: An Indian Perspective (Ministry of Environment, Forest and Climate Change, Government of India, New Delhi, 2018), pp. 1–138. 4. [↵][21]Botswana Vulnerability Assessment Committee, (Botswana Ministry of Local Government and Rural Development, 2019); . 5. [↵][22]1. B. K. Chapman, 2. D. McPhee , Ocean Coast. Manage. 133, 72 (2016). [OpenUrl][23] 6. [↵][24]1. J. A. Santora et al ., Nat. Commun. 11, 536 (2020). [OpenUrl][25] 7. [↵][26]1. L. Towns et al ., Polar Biol. 32, 1529 (2009). [OpenUrl][27][CrossRef][28] 8. [↵][29]1. A. Aryal et al ., Theor. Appl. Climatol. 115, 517 (2013). [OpenUrl][30] 9. [↵][31]1. J. M. Mukeka, 2. J. O. Ogutu, 3. E. Kanga, 4. E. Røskaft , Glob. Ecol. Conserv. 18, e00620 (2019). [OpenUrl][32] 10. [↵][33]1. M. Schiess-Meier, 2. S. Ramsauer, 3. T. Gabanapelo, 4. B. Konig , J. Wildl. Manage. 71, 1267 (2007). [OpenUrl][34] 11. [↵][35]1. S. P. Vargas et al ., Oryx 55, 275 (2021). [OpenUrl][36] 12. [↵][37]1. C. S. Zack et al ., Wildl. Soc. Bull. 31, 517 (2003). [OpenUrl][38] 13. [↵][39]1. J. M. Mukeka et al ., Hum. Wildl. Interact. 14, 255 (2020). [OpenUrl][40] 14. [↵][41]1. P. Variyar , Can Artificial Intelligence Predict Human-Wildlife Conflict? (Wildlife Conservation Trust, 2021); [www.wildlifeconservationtrust.org/can-artificial-intelligence-predict-human-wildlife-conflict/][42]. 15. [↵][43]1. D. Coumou, 2. S. Rahmstorf , Nat. Clim. Chang. 2, 491 (2012). [OpenUrl][44] Acknowledgments: I thank K. Gaynor, A. McInturff, E. Pikitch, and J. Samhouri for valuable discussions and comments. 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The neural ordinary differential equation (neural ODE) model has attracted increasing attention in time series analysis for its capability to process irregular time steps, i.e., data are not observed over equally-spaced time intervals. In multi-dimensional time series analysis, a task is to conduct evolutionary subspace clustering, aiming at clustering temporal data according to their evolving low-dimensional subspace structures. Many existing methods can only process time series with regular time steps while time series are unevenly sampled in many situations such as missing data. In this paper, we propose a neural ODE model for evolutionary subspace clustering to overcome this limitation and a new objective function with subspace self-expressiveness constraint is introduced. We demonstrate that this method can not only interpolate data at any time step for the evolutionary subspace clustering task, but also achieve higher accuracy than other state-of-the-art evolutionary subspace clustering methods. Both synthetic and real-world data are used to illustrate the efficacy of our proposed method.

Computational intelligence-based ocean characteristics forecasting applications, such as Significant Wave Height (SWH) prediction, are crucial for avoiding social and economic loss in coastal cities. Compared to the traditional empirical-based or numerical-based forecasting models, "soft computing" approaches, including machine learning and deep learning models, have shown numerous success in recent years. In this paper, we focus on enabling the deep learning model to learn both short-term and long-term spatial-temporal dependencies for SWH prediction. A Wavelet Graph Neural Network (WGNN) approach is proposed to integrate the advantages of wavelet transform and graph neural network. Several parallel graph neural networks are separately trained on wavelet decomposed data, and the reconstruction of each model's prediction forms the final SWH prediction. Experimental results show that the proposed WGNN approach outperforms other models, including the numerical models, the machine learning models, and several deep learning models.

First unveiled in 2018 at CES, LG is finally bringing its futuristic, rollable LG Signature OLED R 65-inch 4K TV to the US for the hefty sum of $100,000. According to LG's website, the television has an OLED screen, a Dolby Atmos and Sound Pro sound system and has both Google Assistant and Amazon's Alexa for built-in voice control. The 65-inch screen, which is described as a'revolutionary new experience,' gets tucked into aluminum housing unit when not in use, either for watching movies or playing video games. It has an OLED screen, a Dolby Atmos and Sound Pro sound system and both Google Assistant and Amazon's Alexa for built-in voice control The cabinet also has a Dolby Atmos sound system of its own. Manufactured in LG's Gumi facility, each TV is painstakingly assembled'with craftsman-like skill with attention to every detail', LG said last year.

As the Chemistry-Kayak (affectionately known as the ChemYak) swept over the Arctic estuary waters, Victoria Preston was glued to a monitor in a boat nearby, watching as the robot's sensors captured new data. She and her team had spent weeks preparing for this deployment. With only a week to work on-site, they were making use of the long summer days to collect thousands of observations of a hypothesized chemical anomaly associated with the annual ice-cover retreat. The robot moved up and down the stream, using its chemical sensors to detect the composition of the flowing water. Its many measurements revealed a short-lived but massive influx of greenhouse gases in the water during the annual "flushing" of the estuary as ice thawed and receded.

This story was originally published by Wired and is reproduced here as part of the Climate Desk collaboration. An actual space laser is cruising 300 miles above your head right now. Launched in 2018, NASA's ICESat-2 satellite packs a lidar instrument, the same kind of technology that allows self-driving cars to see in three dimensions by spraying lasers around themselves as they roll down the street and analyzing the light that bounces back. But instead of mapping a road, ICESat-2 measures the elevation of Earth's surface with extreme accuracy. Although this space laser means you no harm, it does portend catastrophe. Today in the journal Nature Communications, scientists describe how they used ICESat-2's new lidar data to map the planet's land that's less than 2 meters above sea level, which makes it vulnerable to the creep of sea level rise.

Understanding and representing traffic patterns are key to detecting anomalies in the maritime domain. To this end, we propose a novel graph-based traffic representation and association scheme to cluster trajectories of vessels using automatic identification system (AIS) data. We utilize the (un)clustered data to train a recurrent neural network (RNN)-based evidential regression model, which can predict a vessel's trajectory at future timesteps with its corresponding prediction uncertainty. This paper proposes the usage of a deep learning (DL)-based uncertainty estimation in detecting maritime anomalies, such as unusual vessel maneuvering. Furthermore, we utilize the evidential deep learning classifiers to detect unusual turns of vessels and the loss of AIS signal using predicted class probabilities with associated uncertainties. Our experimental results suggest that using graph-based clustered data improves the ability of the DL models to learn the temporal-spatial correlation of data and associated uncertainties. Using different AIS datasets and experiments, we demonstrate that the estimated prediction uncertainty yields fundamental information for the detection of traffic anomalies in the maritime and, possibly in other domains.