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Identifying climate refugia and bright spots for highly mobile species
Get PDFClimate-driven shifts in species distributions can undermine the effectiveness of protected areas. We present a framework to facilitate climate change adaptation planning by identifying where highly migratory species habitats will persist (climate refugia), emerge (bright spots), disappear (dark spots), or remain unsuitable based on model analysis by 2100. When applied to eight species in the California Current System, we found that, on average, 37% of habitats are expected to be climate refugia, 9% are bright spots, and 13% are dark spots within National Marine Sanctuaries by 2100. Species responses differ: leatherback turtles may find refuge near U.S. coastal waters (18%), blue whales may show increased bright spots (41%), and humpback whales may exhibit more dark spots (44%). These findings highlight the need to integrate species projections into spatial planning to enhance species conservation. Our approach can be applied globally to evaluate the effectiveness of protected areas in safeguarding biodiversity under climate change
Quantifying Marine Carbon Dioxide Removal via Alkalinity Enhancement Across Circulation Regimes Using ECCO-Darwin and 1D Models
Get PDFOcean Alkalinity Enhancement (OAE) is emerging as a viable method for removing anthropogenic (Formula presented.) emissions from the atmosphere to mitigate climate change. To achieve substantial carbon reductions, OAE would need to be deployed at scale across the global ocean. Hence, there is a need to quantify how the efficiency of OAE varies globally across a range of space-time scales in preparation for field deployments. Here we develop a marine carbon dioxide removal (mCDR) efficiency evaluation framework based on the data-assimilative ECCO-Darwin ocean biogeochemistry model, which separates and quantifies two key factors over seasonal to multi-annual timescales: (a) mCDR potential, which quantifies the ability of seawater to store additional carbon after an alkalinity perturbation; and (b) dynamical mCDR efficiency, representing the impact of ocean advection, mixing, and air-sea (Formula presented.) exchange. We apply this framework to virtual OAE deployments in five archetypal ocean circulation regimes with different mCDR potentials and dynamical efficiencies. The simulations highlight the importance of the dynamical factors, especially vertical transport, in driving differences in efficiency. To rapidly isolate and quantify the factors that determine dynamical efficiency, we develop a reduced complexity 1D model, rapid-mCDR. We show that combining the rapid-mCDR model with existing ECCO-Darwin output allows for rapid characterization of OAE efficiency at any location globally. Thus, these tools can be readily employed by research teams and industry to model future field deployments and contribute to essential monitoring, reporting, and verification efforts
Potential of CO2 sequestration through accelerated weathering of limestone on ships
Get PDFCalcium carbonate dissolution is the dominant negative feedback in the ocean for neutralizing the acidity from rising atmospheric carbon dioxide. Mimicking this natural process, the accelerated weathering of limestone (AWL) can store carbon as bicarbonate in the ocean for tens of thousands of years. Here, we evaluate the potential of AWL on ships as a carbon sequestration approach. We show a successful prediction of laboratory measurements using a model that includes the most recent calcite dissolution kinetics in seawater. When simulated along a Pacific shipping lane in the Estimating the Circulation and Climate of the Ocean–Darwin ocean–general circulation model, surface alkalinity and dissolved inorganic carbon increase by \u3c1.4% after 10 years of continuous operation, leaving a small pH and partial pressure of carbon dioxide impact to the ocean while reducing 50% carbon dioxide emission in maritime transportation
On the Application of Long Short-Term Memory Neural Network for Daily Forecasting of PM2.5 in Dakar, Senegal (West Africa)
Get PDFThis study aims to optimize daily forecasts of the PM2.5 concentrations in Dakar, Senegal using a long short-term memory (LSTM) neural network model. Particulate matter, aggravated by factors such as dust, traffic, and industrialization, poses a serious threat to public health, especially in developing countries. Existing models such as the Autoregressive integrated moving average (ARIMA) have limitations in capturing nonlinear relationships and complex dynamics in environmental data. Using four years of daily data collected at the Bel Air station, this study shows that the LSTM neural network model provides more accurate forecasts with a root mean square error (RMSE) of 3.2 μg/m3, whereas the RMSE for ARIMA is about 6.8 μg/m3. The LSTM model predicts reliably up to 7 days in advance, accurately reproducing extreme values, especially during dust event outbreaks and peak travel periods. Computational analysis shows that using Graphical Processing Unit and Tensor Processing Unit processors significantly reduce the execution time, improving the model efficiency while maintaining high accuracy. Overall, these results highlight the usefulness of the LSTM network for air quality prediction and its potential for public health management in Dakar
Aircraft Routing and Crew Pairing Solutions: Robust Integrated Model Based on Multi-Agent Reinforcement Learning
Get PDFEvery year, airlines invest considerable resources in recovering from irregular operations caused by delays and disruptions to aircraft and crew. Consequently, the need to reschedule aircraft and crew to better address these problems has become pressing. The airline scheduling problem comprises two stages—that is, the Aircraft-Routing Problem (ARP) and the Crew-Pairing Problem (CPP). While the ARP and CPP have traditionally been solved sequentially, such an approach fails to capture their interdependencies, often compromising the robustness of aircraft and crew schedules in the face of disruptions. However, existing integrated ARP and CPP models often apply static rules for buffer time allocation, which may result in excessive and ineffective long-buffer connections. To bridge these gaps, we propose a robust integrated ARP and CPP model with two key innovations: (1) the definition of new critical connections (NCCs), which combine structural feasibility with data-driven delay risk; and (2) a spatiotemporal delay-prediction module that quantifies connection vulnerability. The problem is formulated as a sequential decision-making process and solved via a novel multi-agent reinforcement learning algorithm. Numerical results demonstrate that the novel method outperforms prior methods in the literature in terms of solving speed and can also enhance planning robustness. This, in turn, can enhance both operational profitability and passenger satisfaction
Integrating AI into Library Systems: A Perspective on Applications and Challenges
No full textRecent advancements in artificial intelligence (AI) have led to transformative impacts across various sectors, including the library and information science (LIS) domain. The incorporation of AI into traditional and digital library services facilitates the automation of routine tasks such as circulation and cataloging, while simultaneously enhancing patrons\u27 experiences through improved book recommendations and informational chatbots. This perspective paper reviews the literature to identify the current perceptions of AI in libraries, applications of AI in public and academic libraries, future research directions in the field, and the potential challenges of adopting AI in libraries. Through a systematic search and investigation, we detail the purpose, methodologies, and findings of existing literature on AI applications in libraries. This paper documents three main areas of artificial intelligence with potential for integration into library services: recommendation systems, information and resource retrieval, and optical character recognition
A Unifying Double-Reference Approach to Semantic Paradoxes: From the White-Horse-Not-Horse Paradox and the Ultimate-Unspeakable Paradox to the Liar Paradox in View of the Principle of Noncontradiction
Get PDFThe purpose of this study is to suggest and explain an engaging approach to three distinct types of (alleged or genuine) semantic paradoxes, the White-Horse-Not-Horse Paradox, the Ultimate-Unspeakable Paradox, and the Liar Paradox, in a unifying way that is sensitive to distinct features of them. Although the three types of semantic paradoxes address distinct types of objects, and although their seemingly paradoxical features are different (alleged or genuine), their distinct structures and contents can be understood and treated on the same common ground, which is jointly conceived in people\u27s pretheoretic understandings of truth and of the double-reference feature of people\u27s basic employment of language (saying something about an object), and from the unifying vantage point of a double-reference approach
AI-Generated Text Detection and Source Identification
Get PDFThe use of advanced machine learning techniques to detect AI-generated text is a very practical application. The ability to distinguish human-written content from machine-generated text while identifying the source generative model helps address growing concerns about authenticity and accountability in digital communication. The differentiation of human-generated and AI-generated text is highly relevant to several applications, from news media to academic integrity, and is key to ensuring transparency and trust in content-driven environments. However, existing models are often insufficient to accurately detect AI-generated text and determine the specific AI source due to the complex nature of machine-generated content. To address this, it is essential to leverage state-of-the-art machine learning models and embedding techniques that can capture the subtle linguistic and contextual patterns of AI-generated text. In this study, experiments involving text classification were conducted to develop models capable of distinguishing AI-generated content from human-written text and identifying the specific AI model used, offering a multilayered approach to detection. The results demonstrate that the Long Short-Term Memory (LSTM) model with Bidirectional Encoder Representations from Transformers (BERT) embeddings outperformed other embedding techniques at the task of binary classification, achieving a score of 97% for both accuracy and F1 metrics. Additionally, this study illustrates the superior performance of pretrained transformer-based models compared to Recurrent Neural Network (RNN)-based models for four-class source identification, with Robustly optimized BERT approach (RoBERTa) achieving a score of 88% for both accuracy and F1 metrics. This highlights the advantage of leveraging powerful Large Language Models (LLMs) for the complex task of source identification, offering a more robust and scalable solution compared to traditional approaches
Spartan Daily, September 25, 2025
Get PDFVolume 165, Issue 15https://scholarworks.sjsu.edu/spartan_daily_2025/1058/thumbnail.jp