Lakes Environmental (Canada)

This paper presents a probabilistic, multi-layered framework designed to forecast the longevity and security of cryptographic systems under the dual pressures of classical and quantum computational threats. The model integrates thermodynamic decay analogies, stochastic transitions via Hidden Markov Models, and an adapted financial option pricing method to quantify cryptographic degradation, strategic risk, and transition readiness. This framework can guide standardization roadmaps, cipher retirement, or quantum-migration planning, guiding proactive, instead of reactive, crypto agility. Furthermore, it provides a quantitative methodology to complement the current opinions expressed in surveys, as well as a qualitative approach to cryptographic security risk projections.

This essay examines the structural distortions and power asymmetries in China’s online food delivery market, focusing on the rise of Meituan and the subsequent hypercompetitive environment intensified by subsidy wars. While platforms such as UberEats and DoorDash in Western markets fostered balanced competition between restaurants, platforms, and consumers, the Chinese market evolved differently. Meituan consolidated monopoly power during the COVID era through commission fees, delivery charges, and exposure-based advertising schemes that disproportionately burdened small and medium restaurants. The later entry of Taobao and JD, rather than alleviating this imbalance, introduced aggressive subsidy strategies funded partly by merchants, further eroding profitability and creating conditions of negative gross margins. Consumers benefited from low prices, and platforms leveraged data advantages, but restaurants were reduced to expendable actors in a distorted market equilibrium. By analyzing market concentration, cross-subsidy pricing, and coupon-funded competition, this study highlights how unchecked platform dominance undermines industry sustainability and social welfare.

Keeping track of air quality is paramount to issue preemptive measures to mitigate adversarial effects on the population. This study introduces a new quantum–classical approach, combining a graph-based deep learning structure with a quantum neural network to predict ozone concentration up to 6 h ahead. The proposed architecture utilized historical data from Houston, Texas, a major urban area that frequently fails to comply with air quality regulations. Our results revealed that a smoother transition between the classical framework and its quantum counterpart enhances the model’s results. Moreover, we observed that combining min–max normalization with increased ansatz repetitions also improved the hybrid model’s performance. This was evident from evaluating the assessment metrics root mean square error (RMSE), coefficient of determination (R2) and forecast skill (FS). Values for R2 and FS for the horizons considered were 94.12% and 31.01% for the 1 h, 83.94% and 48.01% for the 3 h, and 75.62% and 57.46% for the 6 h forecasts. A comparison with the existing literature for both classical and QML models revealed that the proposed methodology could provide competitive results, and even surpass some well-established forecasting models, proving to be a valuable resource for air quality forecasting, and thus validating this approach.

ABSTRACT Accurate detection of foreign objects during industrial coal preparation is crucial to ensuring the safety of equipment and personnel, as well as maintaining the green utilization of the coal product. The complexity of industrial coal preparation environments presents challenges for vision-based foreign object detection. This work introduces the SHA-DH-YOLOX algorithm, designed to enhance detection accuracy. The proposed algorithm boosts three notable improvements. First, the Shuffle Attention mechanism (SHA) is integrated into the YOLOX backbone to strengthen the feature extraction of essential information from input images. Second, the Dynamic head (Dyhead) is introduced into the feature fusion to enhance the detector’s representation capability, improving scale-, spatial-, and task-awareness. Third, the original Intersection over Union (IoU) loss function is replaced with SCYLLA-IoU (SIoU) to achieve more accurate bounding boxes and enhanced training stability. These improvements work collaboratively with YOLOX-M, resulting in it outperforming state-of-the-art baseline models, including YOLOv5, YOLOv6, YOLOv7, YOLOv8, and original YOLOX. The developed SHA-DH-YOLOX algorithm improves AP50 by 1.87 to 7.97% compared to baseline models of equivalent size. Robustness tests further affirm the stability of the SHA-DH-YOLOX model when facing diverse and challenging scenarios. This pioneering work provides valuable tools for achieving safe and reliable coal preparation practices.

El elefante marino del sur (Mirounga leonina) tiene una distribución circumpolar en el hemisferio sur. Sin embargo, observaciones recientes han documentado esta especie en varios lugares del hemisferio norte. Con la integración de la ciencia ciudadana, se reportan 3 nuevos registros de esta especie en México. Se recopilaron fotografías, videos e información de elefantes marinos con la colaboración de ciudadanos (pescadores y fotógrafas) en el Golfo de California y la costa sur del Pacífico mexicano. De los avistamientos se obtuvo información sobre la fecha, hora, ubicación geográfica y comportamiento general del elefante marino. La especie, sexo y categoría de edad de los pinnípedos se identificaron con base en características morfológicas externas. Se registraron 3 avistamientos de individuos diferentes de elefante marino del sur en las costas del océano Pacifico de México. En febrero de 2021 se observó un macho juvenil en Bahías de Huatulco, Oaxaca. En mayo de 2022 se avistó un macho subadulto en la Bahía de San Jorge, Sonora. En febrero de 2023 se observó otro macho subadulto en el Golfo de Santa Clara, Sonora. Documentamos 3 avistamientos extralimitales del elefante marino del sur, uno de los cuales es el registro más septentrional de esta especie en el Océano Pacífico. Estos registros contribuyen a la comprensión de los patrones de dispersión de los elefantes marinos del sur y resaltan la importancia de la ciencia ciudadana para avanzar en el conocimiento de las distribuciones de los mamíferos marinos.

The impacts of biosolids land application on soil phosphorus and subsequent vertical migration to tile drainage were assessed in a laboratory setup. Soil, representing typical “nonresponse” Ontario soil as specified by Ontario Ministry of Agriculture, Food, and Rural Affairs (OMAFRA), was amended with anaerobically digested biosolids at a rate of 8Mgha−1 (dry weight). Over five months, these amended soil samples from two different depths were sequentially fractionated to determine various inorganic and organic phosphorus pools in order to evaluate phosphorus vertical migration within a soil profile. Soil leachate was analyzed for soluble reactive phosphorus.The results indicated that biosolids application did not significantly affect phosphorus concentrations in soil and did not cause phosphorus vertical migration. The concentrations of soluble reactive phosphorus also were not significantly affected by biosolids.

Floods are one of the most lethal natural disasters. It is crucial to forecast the timing and evolution of these events and create an advanced warning system to allow for the proper implementation of preventive measures. This work introduced a new graph-based forecasting model, namely, graph neural network sample and aggregate (GNN-SAGE), to estimate river flooding. It then validated the proposed model in the Humber River watershed in Ontario, Canada. Using past precipitation and stage data from reference and neighboring stations, the proposed GNN-SAGE model could estimate the river stage for flooding events up to 24 h ahead, improving its forecasting performance by an average of 18% compared with the persistence model and 9% compared with the graph-based model residual gated graph convolutional network (GNN-ResGated), which were used as baselines. Furthermore, GNN-SAGE generated smaller errors than those reported in the current literature. The Shapley additive explanations (SHAP) revealed that prior data from the reference station was the most significant factor for all prediction intervals, with seasonality and precipitation being more influential for longer-range forecasts. The findings positioned the proposed GNN-SAGE model as a cutting-edge solution for flood forecasting and a valuable resource for devising early flood-warning systems.