Healthcare systems are increasingly recognized as significant contributors to environmental burdens; however, conventional lifecycle assessment (LCA) primarily quantifies impacts and offers limited insight into the complex interactions influencing sustainability-related decisions. This study presents a scenario-based LCA of peritoneal dialysis (PD) delivery systems and explores the integration of LCA with dynamically calibrated Fuzzy Cognitive Mapping (FCM) as a systems-oriented framework. The framework is applied to PD delivery systems in Vancouver, Canada, comparing automated peritoneal dialysis (APD) and continuous ambulatory peritoneal dialysis (CAPD). Environmental impacts are assessed using the ReCiPe 2016 midpoint method across full lifecycle stages, including consumables production, transportation, and waste treatment. Multiple scenarios are developed to capture variations in clinical prescription, consumables manufacturing, transportation logistics, and service delivery pathways. Results indicate that APD with standard prescription exhibits the highest total climate change impact (35%), followed by APD incremental (25%), CAPD standard (22%), and CAPD incremental (16%). Across all scenarios, consumables production (>51%) and transportation (>42%) are the dominant contributions, highlighting upstream supply-chain processes as key intervention points. The exploratory calibrated FCM analysis identifies consumables material composition and landfill disposal as influential system components. It ranks CAPD incremental with fully virtual follow-up visits as environmentally favorable pathways under the evaluated system conditions, where clinically appropriate. These findings show that scenario-based LCA can identify clear environmental hotspots, while calibrated FCM provides an exploratory systems perspective for interpreting environmental interactions, identifying influential system components, and exploring potential mitigation opportunities within the PD system.
Alumina (Al2O3) cutting tools are the most commonly used ceramic cutting tools for advanced high-speed machining due to their good mechanical properties and low cost. However, monolithic alumina cutting tools generally suffer due to their low fracture toughness and poor thermal shock resistance, which diminishes the tool life and performance for critical applications. Accordingly, ceramic matrix composites have been developed to replace monolithic alumina. Boron carbide (B4C) is a potentially attractive reinforcement due to its extremely high hardness, low density, and high-temperature resistance. However, the sinterability of Al2O3-B4C composites is limited due to the formation of the deleterious Al18B4O33 phase, which restricts densification. Within the scope of this work, the influence of Y2O3 and graphite additions on the microstructure, mechanical properties, and wear resistance of an Al2O3-30B(4)C composite fabricated using spark plasma sintering has been systematically studied. While Y2O3 addition is beneficial to overall densification, sinterability decreases with increasing graphite addition. The microstructures of the composites display uniform reinforcement distribution, and the presence of B4C reinforcements results in significant Al2O3 matrix grain refinement. The developed composites possess much improved mechanical properties in comparison to monolithic Al2O3, and the following highest mechanical properties have been achieved: Young's modulus of 397 +/- 10 GPa, hardness of 22.7 +/- 1 GPa, indentation fracture toughness of 6.5 +/- 0.5 MPa & sdot;root m , and flexural strength of 572 +/- 52 MPa. The composite developed with 5 vol.% Y2O3 addition has yielded the best wear resistance, with the specific wear rate one-fourth that of monolithic alumina and the average coefficient of friction almost two-thirds that of monolithic alumina. Finally, considering the relative influence of different properties on the overall cutting performance, a simple expression has been established to rank the performance of different compositions developed.
Abstract Conventional drinking water monitoring relies on laboratory-based chemical and microbiological analyses that are often time intensive. This study evaluates a rapid optical alternative by integrating excitation–emission matrix (EEM) fluorescence spectroscopy, parallel factor analysis (PARAFAC), and machine learning to predict intact cell count (ICC) within a chlorinated drinking water distribution system. A full scale network in the Okanagan Valley, Canada was monitored, and fluorescence-derived dissolved organic matter (DOM) components were used as predictors in tree-based classification models. Fluorescence alone showed moderate skill in predicting ICC, but incorporating contextual factors (chlorine residual and turbidity) markedly improved performance. The ICC models could categorically distinguish microbial levels within the distribution system, though absolute accuracy declined at sites not seen during training, indicating a need for site-specific calibration. Notably, this work is the first to assess fluorescence-based metrics for determining ICC in drinking water distribution systems. The findings demonstrate that a unified fluorescence-PARAFAC approach can provide timely alternative measures for water quality, as it shows promise for microbial risk indication when augmented with operational data. Overall, the work highlights the potential of fluorescence-informed machine learning to bridge conventional chemical and microbiological monitoring, offering a pathway toward more responsive water quality management.
Quantifying animal movements is necessary for answering a wide array of research questions in ecology and conservation biology. Consequently, ecologists have made considerable efforts to identify the best way to estimate an animal’s home range, and many methods of estimating home ranges have arisen over the past half century. Most of these methods fall into two distinct categories of estimators that have only recently been described in statistical detail: those that measure range distributions (methods such as Kernel Density Estimation that quantify the long-run behavior of a movement process that features restricted space use) and those that measure occurrence distributions (methods such as Brownian Bridge Movement Models and the Correlated Random Walk Library that quantify uncertainty in an animal movement path during a specific period of observation). In this paper, we use theory, simulations, and empirical analysis to demonstrate the importance of applying these two classes of space use estimators appropriately and distinctly. Conflating range and occurrence distributions can have serious consequences for ecological inference and conservation practice. For example, in most situations, home-range estimates quantified using occurrence estimators are too small, and this problem is exacerbated by ongoing improvements in tracking technology that enable more frequent and more accurate data on animal movements. We encourage researchers to use range estimators to estimate the area of home ranges and occurrence estimators to answer other questions in movement ecology, such as when and where an animal crosses a linear feature, visits a location of interest, or interacts with other animals. Open Research Statement Tracking data on Aepyceros melampus, Beatragus hunteri, Bycanistes bucinator, Cerdocyon thous, Eulemur rufifrons, Glyptemys insculpta, Gyps coprotheres, Madoqua guentheri, Ovis canadensis, Propithecus verreauxi, Sus scrofa, and Ursus arctos are publicly archived in the Dryad repository (Noonan et al. 2018; https://doi.org/10.5061/dryad.v5051j2), as are data from Procapra gutturosa (Fleming et al. 2014a; https://doi.org/10.5061/dryad.45157). Data on Panthera onca were taken from (Morato et al. 2018). Additional data are publicly archived in the Movebank repository under the following identifiers: Canis latrans, 8159699; Canis lupus, 8159399; Chrysocyon brachyurus, 18156143; Felis silvestris, 40386102; Gyps africanus, 2919708; Lepus europaeus, 25727477; Martes pennanti, 2964494; Panthera leo, 220229; Papio cynocephalus, 222027; Syncerus caffer, 1764627; Tapirus terrestris, 443607536; Torgos tracheliotus, 2919708; and Ursus americanus, 8170674.
We present EBFT, an egalitarian framework of BFT consensus for permissioned blockchains. The key innovation in EBFT is egalitarian block generation: instead of relying on a fixed leader, nodes randomly and non-interactively propose blocks, which makes the system resilient to attacks targeting the leader. By combining the longest-chain rule from Nakamoto consensus with quorum voting from classical BFT, EBFT achieves deterministic safety with a design as simple as Nakamoto consensus. To demonstrate its practicality, we build three protocols: EBFT-SYN for synchronous networks, EBFTPSYN for partially synchronous networks, and EBFT-TURBO, a variant that incorporates Bitcoin-NG style decoupling to demonstrate the framework's extensibility towards high performance. We implement EBFT atop the Go version of Bitcoin, btcd. We implemented EBFT-SYN, EBFT-PSYN and EBFT-TURBO in about 920 LoCs in total. This indicates that EBFT can be built on top of existing blockchains with relatively little effort. We evaluate these protocols on EC2 instances with up to 256 nodes. Our evaluation shows that EBFT-SYN (resp. EBFT-PSYN) achieves a latency of 6 (resp 1) seconds. EBFT-TURBO can process up to 3.2k transactions per second and has a latency of 8 seconds.
