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At least 1,477 records · Page 82

Valued peaks: Sustainable water allocation for small hydropower plants in an era of explicit ecological needs

Optimizing hydropower operations to balance economic profitability and support functioning ecosystem services is integral to river management policy. In this article, we propose a dynamic, constrained optimization framework for small hydropower plants (SHPs) to evaluate trade-offs between economic profitability and socio-ecological requirements. Specifically, we examine the balance between short-term losses in hydropower generation and the potential for compensatory benefits in the form of revenue from recreational ecosystem services, irrespective of the direct beneficiary. Our framework integrates a fish habitat model, a hydropower optimization model, and a recreational ecosystem service estimate to evaluate different environmental flow scenarios. The optimization process gives three outflow release scenarios, informed by previous streamflow realisations (dam inflow), and designed environmental flow constraints. The framework is applied and tested for the river Kuusinkijoki in North-eastern Finland, which is a habitat for migratory brown trout and grayling populations. We show that the revenue loss due to the environmental flow constraints arises through a reduction in revenue per generated energy unit and through a reduction in turbine efficiency. Additionally, the simulation results reveal that all the designed environmental flow constraints cannot be met simultaneously. Under the environmental flow scenario with both minimum flow and flow ramping rate constraints, the annual hydropower revenue decreases by 16.5 %. An annual increase of 8 % in recreational fishing visits offsets the revenue loss. In conclusion, the developed framework provides knowledge of the costs and benefits of hydropower environmental flow constraints and guides the prioritizing process of environmental measures.

13 HYDRO ENERGY↗

Innovative high-temperature ammonium bifluoride fusion and rapid analysis of elements with nuclear forensic value

High-temperature ammonium bifluoride (ABF) fusions were evaluated for potential use in rapid dissolution of post-detonation nuclear debris. The ABF fusion was carried out in a Pt crucible which allowed evaluation of higher fusion and evaporation temperatures. The high-temperature ABF fusion dissolution method was evaluated using geological reference materials: USGS QLO-1a Quartz Latite, USGS SDC-1 Mica Schist, and NIST 278 Obsidian Rock. The optimized dissolution method involved a 10 min fusion at 540 °C, a 5 min reflux in 8 M HNO 3 , an evaporation at 300 °C and final dilution into 45 mL of 2% (v/v) HNO3. The final solution was filtered after heating at 105 °C using a hotblock. This dissolution method was simple, requiring only a hotplate or hotblock, filtered samples were available for ICP-MS analysis or radiochemical separation within 150 min, and was found to have high (>90%) recovery for many isotopes of interest in nuclear forensics applications. U and Pu in the dissolved material was separated using TEVA and UTEVA extraction chromatography columns, a process which resulted in >90% recovery. An irradiated U tracer was spiked into the material prior to dissolution and analyzed for recovery of major fission products and 239Np. The monitored radionuclides had recoveries of greater than 90%, except for the volatile radioiodine isotopes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The value of human data annotation for machine learning based anomaly detection in environmental systems

Anomaly detection is the process of identifying unexpected data samples in datasets. Automated anomaly detection is either performed using supervised machine learning models, which require a labelled dataset for their calibration, or unsupervised models, which do not require labels. While academic research has produced a vast array of tools and machine learning models for automated anomaly detection, the research community focused on environmental systems still lacks a comparative analysis that is simultaneously comprehensive, objective, and systematic. This knowledge gap is addressed for the first time in this study, where 15 different supervised and unsupervised anomaly detection models are evaluated on 5 different environmental datasets from engineered and natural aquatic systems. To this end, anomaly detection performance, labelling efforts, as well as the impact of model and algorithm tuning are taken into account. As a result, our analysis reveals the relative strengths and weaknesses of the different approaches in an objective manner without bias for any particular paradigm in machine learning. Most importantly, our results show that expert-based data annotation is extremely valuable for anomaly detection based on machine learning.

54 ENVIRONMENTAL SCIENCES↗

Overexpressed ACBD3 has prognostic value in human breast cancer and promotes the self-renewal potential of breast cancer cells by activating the Wnt/beta-catenin signaling pathway

Highlights: • Up-regulated ACBD3 is associated with clinicopathological featuresin breast cancer. • ACBD3 regulates the enrichment of BCSCs via Wnt/beta-catenin pathway. • ACBD3 may represent candidate therapeutic targets to enable the elimination of BCSCs. Acyl-CoA binding domain containing 3 (ACBD3) is involved in the maintenance of Golgi structure and function through its interaction with the integral membrane protein. However, the clinical significance and biological role of ACBD3 in breast cancer remain unclear. Herein, we found that the mRNA and protein levels of ACBD3 were markedly up-regulated in breast cancer cells and tissues. Immunohistochemical analysis of breast cancer tissues demonstrated that ACBD3 overexpression was significantly associated with advanced clinicopathological features. Univariate and multivariate analysis indicated that ACBD3 overexpression correlates with poor prognosis in breast cancer. Furthermore, overexpressing ACBD3 promoted, while silencing ACBD3 inhibited, self-renewal and tumorigenesis in breast cancer cells in vitro and in vivo respectively. Importantly, upregulating ACBD3 promoted the self-renewal and tumorigenesis of breast cancer cells via activating the Wnt/beta-catenin signaling, and the pro-self-renewal effect of ACBD3 in breast cancer was antagonized by the Wnt signaling inhibitor TCF4-siRNA and Lef1-siRNA.These findings indicate that ACBD3 may represent candidate therapeutic targets to enable the elimination of breast cancer stem cells, providing the preclinical proof-of-concept for the prevention and treatment of breast cancer.

60 APPLIED LIFE SCIENCES↗