Bing Chen

Blue-green algal blooms are a major problem in bodies of water which can lead to deterioration of drinking water quality and to human health issues. Cyanobacteria, which have high concentrations in blue-green algal blooms, can produce harmful intracellular toxins (cyanotoxins, such as microcystins) which can cause skin and eye irritation, nausea, or more severe health issues upon ingestion. Therefore, it is important to be able to detect high cyanobacteria levels in bodies of water, especially when they are used as sources of drinking water. Traditional methods use in-situ sensors to measure levels of chlorophyll-a and phycocyanin, to determine the presence of such blooms. However, the deployment of such sensors is costly and also sometimes cumbersome. Recently, the use of satellite imagery has become another tool to determine the presence of algal blooms. The advantage of using such imagery is the large coverage area when compared to in-situ sensors, as well as their free availability. In this paper, we describe a study at Forest Pond, Newfoundland, Canada, where prediction of chlorophyll-a and phycocyanin levels is made and compared to measured levels from in-situ sensors. A machine learning method based on deep neural networks is introduced which is shown to determine such levels with fair accuracy as well as reduce the risk of false negatives when predicting the presence of blue-green algal blooms. This work has the potential to significantly reduce water testing costs by providing an alarm system for the presence of blue-green algae in bodies of water without the need for widespread in-situ testing, instead allowing testing to be performed only in a targeted manner where algal levels have been flagged by the alarm system as potentially being high.

Vehicle-based measurement systems occupy an important niche in the multiscale methane measurement landscape between component-level technologies and aerial-based methods. Performance evaluations are often limited to one dimension (e.g., quantification), while methodological opacity limits interpretation and intercomparisons between technologies. Here, we develop a vehicle-based measurement unit employing repeated site-perimeter transects and a processing framework featuring a novel weighted-likelihood algorithm for automated source attribution. Using controlled release experiments at two dedicated testing facilities, we characterize system performance across three dimensions─detection, attribution, and quantification. The system achieved a 94.5% true-positive detection rate with no false positives and a 90% detection limit as low as 0.078 kg/h with five repeat transects. Attribution accuracy was high at the source-group level, reaching 99.7% in single-source releases and 86% in multisource scenarios. Quantification was unbiased for low-to-moderate complexity releases (2 source groups; slope = 0.99, R2 = 0.81) but overestimated under highly complex conditions (5 source groups; slope = 1.45, R2 = 0.91). These results provide a transparent performance benchmark, demonstrating that these systems are well-suited to survey the large populations of low-to-moderate-complexity sites (e.g., single wells and batteries), as a screening layer in LDAR applications, and to quantify the low end of emission distributions in inventories, reducing reliance on generic emission factors.

2.5D C/SiC composites are characterized by high hardness and brittleness. These properties render the composites prone to fiber fracture, burr formation and matrix damage during grinding for hole-making. This study systematically investigates the material removal mechanism, tool wear behavior and machining quality evolution process during diamond core drill grinding for hole-making. Through experiments, the effects of grinding angle and grinding force and the thermal effects on material removal characteristics and hole wall machining quality were analyzed, and the stagewise characteristics of tool wear and the correlation between processing parameters and machining quality were clarified. The results indicate that the wear of diamond core drills undergoes a three-stage evolution: slight abrasive grain shedding at the initial stage, local damage and wear loss at the middle stage, and large-scale abrasive grain peeling followed by tool failure at the late stage. As wear aggravates, the machining axial force increases remarkably and the grinding temperature rises drastically. Hole wall defects gradually develop from minor initial fiber fracture into complex failure modes including burrs, fiber pull-out and matrix damage. In particular, the morphological degradation at the hole exit is the most severe. This study verifies that the optimization of process parameters and tool design can improve machining quality, and provides theoretical guidance and an experimental basis for the efficient and precise machining of high-performance composites.

The objective of this study is to address the issues of low processing efficiency in band sawing of variable cross-section materials under constant feed rate, as well as the imprecise control caused by response lag and current fluctuations in traditional variable feed control. In order to achieve this objective, a dynamic feed rate optimization strategy is hereby proposed. This strategy is based on the integration of multiple processing parameters. The proposed method establishes a real-time mapping relationship between sawing depth and cross-sectional width, thus taking into account material shape and size, single-tooth force constraints, and the remaining life of the band saw blade. The development of a feed rate curve control model is presented, with the objective of enhancing both processing efficiency and stability. Edge-based deployment tests demonstrate that the proposed strategy effectively adapts to complex cross-sectional variations. Compared with constant-speed sawing, the optimized method improves machining efficiency by 10.66% while maintaining a relatively stable load current during the main cutting stage. These results indicate that the proposed strategy can enhance machining efficiency and process stability, and reduce the risk of excessive cutting load and band saw blade overload.

Marine oil spills can contaminate beaches, with ecological impacts and operational challenges extending long after on-water recovery has ended. Predicting oil remobilization and retention can help improve shoreline treatment efficacy, reduce environmental impacts, and support a more effective response. This laboratory study examined the 24-hour remobilization and retention of Very Low Sulphur Fuel Oil and diluted bitumen in five uniform sediments across seven temperature regimes during a single simulated tidal cycle following an 8-hour penetration phase, using more than 435 column experiments. Across screened experiments, 24-hour oil retention ranged from 52 to 99% and was strongly dependent on sediment type and temperature. Residual oil concentration decreased exponentially with increasing grain size, with strong model performance under uniform temperature conditions (R2=0.963) and somewhat greater variability under non-uniform regimes (R2=0.831). Time-resolved oil remobilization in coarse sand and very coarse sand exhibited a reliable two-stage response: a rapid release of hydraulically accessible oil, followed by a slow release of capillary-trapped oil. Oil concentrations after 24 hours decreased with increasing sediment grain size and decreased with increasing oil viscosity, whether intrinsic to the product or modified by temperature or weathering. Oil mass retained within the sediments increased proportionally with penetration depth, such that deeper penetration of a given oil into a sediment resulted in greater mass retention. This study provides a mechanistic interpretation of oil remobilization and retention in shoreline sediments, demonstrating that these processes are influenced by temperature, with implications for optimizing shoreline treatment options and prioritizing response actions across climate-relevant temperature regimes.

The band saw blade is distinguished by its multi-point and flexible cutting capability when sawing materials. Its wear form is significantly more intricate than that of traditional cutting tools, such as the lathe tool and the milling cutter. Preliminary experimental observations suggest a close correlation between the wear of band saw blades and the motor current of the driving wheel. Therefore, this study evaluates the wear condition of band saw blades using current signals. A mathematical correlation model was established between the driving wheel motor current signals and the load on the band saw. A comprehensive experimental study was conducted on the band saw blade, encompassing the entire lifecycle of sawing operations. The average wear width of the tooth tip was utilized as an indicator of tooth wear, and an investigation was conducted into the correlation between the driving wheel motor current signals and the wear state. The findings indicated that the driving wheel motor current signals could be utilized to assess the blade wear state with high precision, which would facilitate proactive maintenance and replacement strategies to optimize band saw performance and service life.