Although sorption is crucial for removing contaminants of emerging concern (CECs) in on-site wastewater treatment systems (OWTS), and biofilms develop extensively within drainfields, little is known about the effects of sorption and biosorption (with biofilm) on contaminant fate. To gain insights, the transport of triclosan (TCS) and benzalkonium chloride (BAC) (hydrophobic and amphiphilic antimicrobials, respectively) was studied in saturated sand columns with and without 1-day-old and 3-day-old biofilms. Triclosan S-shaped breakthrough curves (BTCs) indicated cooperative sorption, and BAC two-step BTCs suggested irreversible sorption with a maximum capacity. One-day-old biofilms increased TCS retardation while showing no effect for BAC. The 3-day-old biofilms further increased TCS retardation and decreased BAC retardation. Therefore, early stage biofilms can affect contaminant sorption in as little as 1 day and add new TCS sorption sites, whereas hindering BAC sorption. Quartz crystal microbalance with dissipation (QCM-D) using SiO2 and Fe2O3 sensors showed higher protein deposition than humic acids and polysaccharides at pH 7, suggesting that proteins drive extracellular polymeric substance (EPS) deposition. Higher BAC deposition on the clean SiO2 sensor than on the EPS-coated sensor revealed that EPS likely impaired BAC electrostatic interactions with the surface. These findings support that biofilms affect contaminant mobility and highlight the need for considering biosorption in optimizing OWTS design.
Project delivery systems (PDSs) play a crucial role in determining a construction project's success, yet their selection often relies on subjective judgements, leading to issues such as delays, cost overruns and quality deficiencies. While several tools have been developed to guide this decision, owner qualifications, available resources and project scheduling were often overlooked, despite their strong correlation with the PDS selection. This study proposes a decision support tool combining PDS selection and project scheduling through a multi-objective optimization model. The tool assists in assigning the most suitable PDS for each construction project while considering human resources and deadlines. The tool also includes both traditional and collaborative PDSs to align with evolving industry needs. A public infrastructure case study demonstrated the tool's effectiveness by evaluating multiple scenarios and analysing the impact of PDS selection and scheduling decisions on the project completion. Results highlight the influence of owner preferences and project objectives on the outcomes, emphasizing the need to align decisions with project goals. By integrating PDS selection with project scheduling, this research fills a gap in the literature and offers a practical, data-driven approach to improving decision-making in construction project management.
While nitrogen supply is known to enhance the growth of boreal conifers, species-specific plastic responses of biomass partitioning and functional traits to nitrogen availability in seedlings – critical factors for their growth and survival in a changing environment – remain understudied. Here, we conducted a 15-month-greenhouse experiment to investigate how nitrogen availability affects growth, biomass partitioning, bud production, and root traits in seedlings of three widespread boreal conifer species. Black spruce seedlings had intermediate biomass partitioning values between tamarack and jack pine. Tamarack had the highest stem mass fraction (SMF) and relative height growth, but the lowest aboveground mass fraction (AGMF). Jack pine had the highest leaf mass fraction (LMF), LMF/SMF ratio and AGMF. Specific root length was higher in black spruce (10-13 m g-1) than in tamarack (8-9 m g-1). Nitrogen fertilization 1) decreased root mass fraction in all species, particularly in tamarack, consistent with the optimal partitioning theory; 2) increased root diameter by 20-25
Software analytics leverages machine learning models to extract insights from historical data on software projects. These models come with configurable parameters, known as hyperparameters, which govern their characteristics, such as the number of trees in a random forest. Hyperparameter optimization is crucial for achieving optimal performance in several software engineering problems, such as software defect prediction (SDP). To perform hyperparameter optimization, an appropriate tuning metric should be set to guide the optimal hyperparameter settings. However, the impact of the chosen tuning metric on models’ performance remains unexplored. In this paper, we address this gap by examining the impact of the hyperparameter tuning metric on the performance of software analytics models, using SDP as a case study. First, we start by investigating 105 previously published SDP studies to understand whether researchers report the employed tuning metrics. To further understand the impact of hyper-parameter tuning metrics on model performance, we conduct an empirical study on an SDP dataset comprising 28 releases, by tuning and evaluating 4 widely-used models using 8 tuning metrics and 3 common performance metrics. Our literature review reveals that researchers report the used tuning metric in only 29
In the present project, we studied two aluminum alloys: A319.1 and A413.1. Different metallurgical parameters were applied for each alloy. These parameters included degassing, strontium addition, TiB2 addition, and the amount of hydrogen. Nine different conditions were created for each alloy. All samples underwent solution heating for eight hours at 495 °C for alloys A319.1 and A413.1. Finally, aging was carried out for five hours at 140 °C, 155 °C, 180 °C, 200 °C, 220 °C, and 240 °C. Various examinations were performed on the samples to measure their microstructure and macrostructure, as well as their mechanical properties. Grain size and the morphology of silicon particles and pores were measured to evaluate the microstructure and macrostructure of the alloys. For mechanical properties, hardness and impact strength were measured. The aging of alloys with CuAl2 particles resulted in a better resistance to softening (between 180 °C and 240 °C) compared to those using Mg2Si particles, which exhibited a significant decrease in hardness after reaching their maximum temperature (180 °C). The hardening of the alloys was caused by the precipitation of CuAl2 particles for alloy A319.1. For alloy A413.1, there is a marginal increase in hardness. This is explained by the low copper concentration in the alloy (0.5
