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Spatial richness patterns of large pelagic fishes in the Eastern Pacific Ocean
No full textIn the Eastern Pacific Ocean, the purse seine tuna fishery targets skipjack, yellowfin tuna, and bigeye tuna. However, bycatch of billfish is also common. Growing concerns about the rising bycatch levels and the capture of juveniles’ tunas underscore the need for spatial explicit management strategies. This study aims to model the geographic distributions of four tuna species (skipjack, yellowfin, bigeye, and black skipjack) and five billfish species (swordfish, sailfish, striped marlin, blue marlin, and black marlin) to understand how interannual variability shapes richness patterns of these species to inform spatial management efforts. Species distribution models were developed using MaxEnt based on environmental scenarios representing cold, neutral, and warm conditions from 2000 to 2020. A database of occurrences was compiled from Inter-American Tropical Tuna Commission´s, while environmental variables were obtained from Copernicus Marine Service. Predicted distribution maps for each species were combined to generate species richness maps under different environmental scenarios. Areas of high species richness were identified near the Baja California peninsula, the southern Gulf of California, off the coast of Central America, and between the Ecuadorian coast and the Galapagos Islands, regions that overlap with known spawning and feeding grounds. Species richness patterns showed an interannual variability, expanding during warm periods and contracting during neutral and cold conditions, highlighting the need to adapt spatial management to environmental variability of oceans. These findings provide a basis for future spatial planning and conservation initiatives, highlighting the need for dynamic, flexible conservation strategies that can adapt to a changing ocean
A Curriculum-Based Deep Reinforcement Learning Framework for the Electric Vehicle Routing Problem
No full textThe electric vehicle routing problem with time windows (EVRPTW) is a complex optimization problem in sustainable logistics, where routing decisions must minimize total travel distance, fleet size, and battery usage while satisfying strict customer time constraints. Although deep reinforcement learning (DRL) has shown great potential as an alternative to classical heuristics and exact solvers, existing DRL models often struggle to maintain training stability-failing to converge or generalize when constraints are dense. In this study, we propose a curriculum-based deep reinforcement learning (CB-DRL) framework designed to resolve this instability. The framework utilizes a structured three-phase curriculum that gradually increases problem complexity: the agent first learns distance and fleet optimization (Phase A), then battery management (Phase B), and finally the full EVRPTW (Phase C). To ensure stable learning across phases, the framework employs a modified proximal policy optimization algorithm with phase-specific hyperparameters, value and advantage clipping, and adaptive learning-rate scheduling. The policy network is built upon a heterogeneous graph attention encoder enhanced by global-local attention and feature-wise linear modulation. This specialized architecture explicitly captures the distinct properties of depots, customers, and charging stations. Trained exclusively on small instances with N=10 customers, the model demonstrates robust generalization to unseen instances ranging from N=5 to N=100, significantly outperforming standard baselines on medium-scale problems. Experimental results confirm that this curriculum-guided approach achieves high feasibility rates and competitive solution quality on out-of-distribution instances where standard DRL baselines fail, effectively bridging the gap between neural speed and operational reliability
Quantification of sulfur compound emissions from Sargassum Strandings
No full textMassive strandings of pelagic Sargassum release large quantities of hydrogen sulfide (HS), sulfur dioxide (SO), and dimethyl sulfide (DMS), posing risks to human health and local air quality. Emission factors needed to model and mitigate impacts are currently lacking. This study quantified sulfur emission factors from decomposing sargassum under laboratory conditions. Sargassum and seawater were collected from Key Biscayne, Florida, during summer and winter to investigate seasonal variability and the influence of seawater. Decomposition was monitored in sealed chambers over 14 days, measuring daily total sulfur, HS, and SOusing a UV fluorescence analyzer. DMS was analyzed by gas chromatography-mass spectroscopy (GC-MS). Sulfur emissions peaked within two days of decomposition before gradually declining. Seasonal variation was observed in emission rates, with summer samples exhibiting higher emission rates (average total sulfur peak: 3440 μg/kg-day in summer samples) compared to winter samples (71.2 μg/kg-day). The presence of seawater enhanced sulfur emissions across seasons. The highest total sulfur emission factor was observed in the "summer with seawater" condition, peaking at approximately 5190 μg/kg-day. HS was the dominant sulfur species (29% on average). DMS was the major volatile organic compounds. These findings demonstrate that seasonal factors and environmental conditions are key determinants of sargassum sulfur emissions. The quantified emission factors can inform public health management strategies, highlighting the need for rapid removal of water-saturated sargassum, particularly during summer, to mitigate HS release. The detection of DMS and SOindicates that sargassum strandings are a significant coastal source of aerosol precursors, impacting atmospheric chemistry
Challenges and opportunities in scaling climate-resilient housing solutions in the United States
No full textIntensifying climate-related damages across the United States underscore the importance of climate-resilient housing, which requires coordination across diverse actors in the housing sector. Here, we assess the challenges and opportunities for reducing climate impacts on housing within U.S. coastal communities, based on 64 interviews with experts across housing-relevant public, private, and nonprofit sectors. We provide an overview of risk reduction actions being implemented as well as barriers and enablers to scaling up these responses. We find that current risk reduction actions focus on small-scale property-level adjustments or early-stage advocacy, though experts desire solutions that enable systems-wide reductions of climate-housing risks. Path dependencies, financing, and other entrenched multi-sectoral challenges currently limit resilient housing development. Experts perceive government interventions as essential in enabling resilient housing, and we find that government-led, multi-stakeholder collaborations have already catalyzed action. Understanding these cross-sectoral dynamics can inform actions and pathways to increase climate-housing resilience nationwide
Cost Efficiency and Gender Diversity in Microfinance: A Stochastic Frontier Approach
No full textCost efficiency in microfinance reveals whether microfinance institutions (MFIs) can serve the vulnerable sustainably or survive by relying on subsidies or passing inefficiencies to clients. As MFIs strive for sustainability, the role of gender — where women make up the majority of borrowers and more than one-third of MFIs’ employees — remains central to understanding efficiency. Using data from 1,507 MFIs in 111 countries (2010–2018), we apply cutting-edge panel stochastic frontier models to assess global efficiency trends and the roles of women as borrowers and employees. We find that global efficiency declined, especially among larger MFIs, despite increasing returns to scale. MFIs that serve more women or hire more loan officers are associated with more inefficiencies. The effect of other women employees varies by role and profit orientation. Women in leadership improve efficiency in nonprofits but reduce it in profit MFIs, whereas female staff improve efficiency only in profit MFIs. These findings call for more targeted gender strategies to enhance inclusion and sustainability.
•We use advanced panel stochastic frontiers to assess microfinance cost efficiency.•Despite increasing returns to scale, efficiency declined, especially in larger MFIs.•Women borrowers and loan officers are associated with lower efficiency.•Women staff boost efficiency in profit MFIs but deteriorate it in nonprofits.•Women on boards and managers improve efficiency in nonprofits, worsen it in profits
Corrigendum to “Medical treatment of hypercortisolism with relacorilant: Final results of the phase 3 GRACE study” [American Heart Journal 278S (2024) Pages 10-11]
No full textFactors influencing immediate post-angiographic occlusion outcomes in intracranial aneurysms treated with the woven endobridge device: a multi-center analysis and predictive model from the WorldWideWEB consortium
No full textThe Woven EndoBridge (WEB) device treats wide-necked bifurcation aneurysms, but occlusion rates vary. This study aims to identify factors associated with immediate WEB device occlusion. Data from patients treated with WEB devices across 36 sites were analyzed. Machine learning algorithms and ordinal regression models were developed to predict immediate incomplete occlusion for ruptured and unruptured aneurysms. The study included 1565 patients, with 436 ruptured and 1129 unruptured aneurysms. Immediate complete occlusion was achieved in 38.3% of ruptured and 32.8% of unruptured aneurysms. For ruptured aneurysms, the CatBoost classifier achieved an AUROC of 0.69. Key predictors of incomplete occlusion included pretreatment mRS, aneurysm diameter, and MCA location. Ordinal regression revealed that smoking history (OR: 1.95,
p
< 0.001), neck diameter (Odds Ratio [OR]: 1.50,
p
< 0.001), and presence of a branch from the aneurysm (OR: 2.06,
p
= 0.016) were associated with incomplete, while bifurcation aneurysms (OR: 0.55,
p
= 0.017) were associated with complete immediate occlusion. For unruptured aneurysms, the CatBoost classifier achieved an AUROC of 0.68. Significant predictors of immediate incomplete occlusion included aneurysm neck width, MCA location, and presence of daughter sac. Ordinal regression revealed that smoking history (OR: 1.29,
p
= 0.032), neck diameter (OR: 1.24,
p
< 0.001), and presence of a daughter sac (OR: 1.53,
p
= 0.005) were associated with incomplete, while bifurcation aneurysms (OR: 0.71,
p
= 0.02) and posterior circulation location (OR: 0.68,
p
= 0.01) were associated with complete immediate occlusion. Careful evaluation of patient demographics and specific aneurysm characteristics may help improve the outcomes of intracranial aneurysms treated with WEB device
