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Enhancing mycophytoremediation potential of Chrysopogon zizanioides in chromite-asbestos mine waste soil using arbuscular mycorrhizal fungi: A natural bioaccelerator for soil ecosystem rehabilitation
No full textSoil contamination with toxic elements (TEs) has become a serious environmental issue in recent decades. Bio-based approaches especially, “phytoremediation-associated with arbuscular mycorrhizal fungi (AMF)” has emerged as a promising, eco-friendly and sustainable technology worldwide. The present investigation assessed the impact of AMF on the growth and TEs accumulation abilities of vetiver (Chrysopogon zizanioides) in a soil containing chromite-asbestos mine wastes. Among the four different AMF species tested—Glomus hoi, Funneliformis coronatum, Claroideoglomus claroideum, and Claroideoglomus etunicatum—Glomus hoi (M1) showed high efficiency in improving soil quality, mitigating TEs stress and promoting healthy plant growth. In comparison with control plant (devoid of AMF), the higher accumulation of TEs in the roots (Ni: 27.44 %, Cr: 21.74 %) was observed in presence of Glomus hoi and TEs concentration in soil was reduced in bioavailable phase. A periodic increase in microbial-enzymatic activity was found across all AMF inoculums, with the M1 treatment (microbial biomass carbon: 527.66 mg kg−1) exhibiting highest microbial activity as compared with control. The AMF infection resulted in heightened antioxidant activity, which mitigated TE-induced stress in vetiver plants. Additionally, glomalin production (TG: 2.59 folds), phosphorus uptake and colonization percentage were higher in the vetiver plant inoculated with Glomus hoi compared to the other AMF species. The model-based analysis (PLS-SEM and sobol model) also validates these findings, showing that the presence of AMF enhances phytoremediation efficiency. Overall, this study highlighted that the application of appropriate AMF species can enhance mycophytoremediation potential and provide a viable approach for the rehabilitation of mine-degraded soils
Drivers of metabolism and decomposition in forested Lake Superior tributary streams
No full textEcosystem respiration (ER) and decomposition are fundamental carbon cycling processes in streams. Decomposition and ER may be controlled by similar environmental factors, as decomposition is carried out by heterotrophic bacteria, fungi, and macroinvertebrates, whose metabolic activity is a major contributor to ER. However, ER also includes respiration by autotrophs, and their respiration will be affected by factors that influence gross primary production (GPP). We quantified decomposition, GPP, and ER and estimated autotrophic respiration (AR) and heterotrophic respiration (HR) at eight sites in four forested streams in the Upper Peninsula of Michigan. These streams spanned gradients of canopy cover and dissolved organic carbon (DOC) concentrations, which we predicted would differentially affect activity due to changes in light and carbon availability. We also explored relationships between ER, AR, HR, and decomposition with other environmental drivers like temperature and nutrient availability. ER was related to TP concentrations but not DOC or canopy cover, and AR and HR were not related to any environmental drivers. Decomposition was related to canopy cover and dissolved nutrient concentrations, but not DOC. While our results did not demonstrate direct relationships between canopy cover or DOC concentration and respiration rates, we did find a wide variation in the fraction of respiration carried out by autotrophs relative to other studies, and that decomposition rates were comparable across study sites despite differences in GPP and ER. Together, these results suggest a need to improve methods and estimates of AR and HR in forested streams to integrate these important aquatic ecosystems into global climate models
Nanoscale Structure-Property Relationships of Cyanate Ester as a Function of Extent of Cure
Get PDFCyanate esters are key thermosetting resins for composite materials that require structural integrity and resistance to elevated temperatures. Because cyanate ester composites require relatively high processing temperatures, they are susceptible to the formation of process-induced residual stresses, which compromise their overall strength and durability. Process modeling is a key strategy for optimizing processing parameters to minimize such residual stresses. A necessary component of effective and efficient process modeling of composites is computationally established resin property evolution relationships for a range of processing parameters. In this study, the physical, mechanical, and thermal properties of a cyanate ester resin are established as a function of processing time and temperature using experimentally validated molecular dynamics modeling. The results show that the properties are strongly dependent on the processing temperature. At processing temperatures above 160 °C, the properties quickly approach their fully cured values, whereas at processing temperatures below 140 °C, the chemical cross-linking is significantly inhibited, and processing times to complete cure are relatively long. The evolution of the physical, mechanical, and thermal properties as a function of processing time is established, which is critical data needed as input into multiscale process modeling and optimization of cyanate ester composites for computationally driven composite design
Distribution Systems Clustering Through Voltage Unbalance Sensitivity to Photovoltaic Presence
No full textDistributed photovoltaic micro and mini-generators are gaining prominence as they become more accessible to end consumers. These systems promise to reduce expenses related to electrical energy and offer potential financial returns. One way to enhance the application and planning of the distribution network and its hosting capacity is by partitioning these systems based on the impact of distributed generation on power quality. This study aims to develop a generic network partitioning method using the Spectral Clustering algorithm, enhanced with a balancing algorithm to ensure similar cluster sizes. The input data for this method come from modeling actual standardized electrical systems and simulating the individual impact of photovoltaic generation on each low-voltage feeder connected to the primary grid. The unbalance sensitivity coefficient, which quantifies the change in the voltage unbalance factor at one node due to the variation of active power at another, is used as a metric. Considering all simulation states, the ratios of these coefficients per node form the elements of the Sensitivity matrix. By applying Balanced Spectral Clustering to this Sensitivity matrix, the method can identify continuous electrical regions that highlight the optimal partitioning of the tested distribution system. This proposed method may serve as an initial framework for defining the hosting capacity of smaller regions, taking into account the characteristics of the entire electrical system while managing its restrictive factors locally
DISTRIBUTING COMPOSITION: RHETORICAL AGENCY IN FIRST-YEAR WRITING
Get PDFThis project centers on engaging students in class lessons related to rhetorical agency. Typically understood as the capacity to act, within rhetoric, I define agency as the capacity to make meaning. In most traditional Western conceptions, agency is primarily located with the individual rhetor. However, these views were increasingly challenged throughout the later twentieth century as theorists from various traditions came to understand that discussing our agency as solely our own doing was insufficient. In contrast to an individualistic understanding of agency, theories that characterize rhetorical agency as more fragmented have evolved, where, instead of writing and rhetoric being within the control of the individual, multiple actors co-constitute a rhetorical event. These views are what I refer to as distributed agency. Distributed ways of theorizing agency present a challenge for writing instruction because pedagogical applications of rhetoric often depend on the characterization of agency as something that can be possessed by the individual writer. In light of these concerns, my project is an attempt to investigate how First-Year Writing (FYW) students conceptualize rhetorical agency and how they view the relationship between their understanding of agency and their writing processes. I introduced distributed agency to students in my FYW course through four lessons drawing on existing theories of rhetorical agency, centered on the key concepts of ecology, intersubjectivity, materiality, and indeterminacy. In this project, agency represents a way of thinking about writing processes through developing an understanding of what agents influence writing and how to account for those impacts. A classroom research project brings a particular kind of perspective to discussions of agency because while the literature emphasizes that distributed agency presents a problem for FYW, there has not yet been a thorough investigation into student perceptions of rhetorical agency. Through coding of students’ reflective writing, my results show that while most students came into my FYW course with a traditional understanding of agency as located with the individual writer, incorporating these lessons helped students better appreciate the concept of distributed agency over the course of the semester. However, students did not actively view rhetorical agency as something that impacts their writing process. While they increased their understanding of agency, they were not able to apply this knowledge to their writing choices. I offer some future directions, including possibilities to help students translate insights they develop in the lessons into writing process knowledge, and I examine the limitations of what reflection can reveal about student learning
ThermalTrack Dataset - Testing Images
Get PDFWe present a wheel track detection system that leverages RGB-Thermal (RGB-T) imaging, where thermal channels reveal critical temperature differentials between compacted tracks and loose snow - tracks exhibit higher thermal inertia and lower reflectivity, emitting stronger radiation signatures even in visually homogeneous conditions. By fusing these distinctive thermal patterns with RGB spatial information, our method reliably identifies navigable tracks, enabling robust path-following in complete white-out conditions where snow textures and terrain features become indistinguishable
Impact of Eco-Friendly Surfactant Structure and Class on Enveloped Virus Inactivation
No full textBACKGROUND: Sustainable and effective strategies for virus inactivation are crucial for ensuring the safety and quality of biological products. The European Union\u27s (EU) 2021 ban on Triton X-100 for viral inactivation in biomanufacturing has pushed the field to find sustainable alternatives with equal effectiveness. We aim to increase the sustainability of biopharmaceutical production by ensuring the effectiveness of eco-friendly surfactant-mediated virus inactivation by comparing the antiviral efficacy of Triton X-100 to glucosides and amine oxides. RESULTS: Surfactants were evaluated for antiviral efficacy against herpes viruses, SuHV and HSV, and the retrovirus XMuLV. The surfactants demonstrated equivalent or superior inactivation efficacy compared to Triton X-100. Herpes viruses were inactivated similarly with all surfactants. For XMuLV, surfactants with longer alkyl chains achieved maximum log reduction values (LRV) at 1x CMC, outperforming Triton X-100, which required 2x CMC for comparable efficacy. Surfactants with bulky headgroups, such as LAPAO, showed lower efficacy against XMuLV. At a salt concentration of 2 M ionic strength, the antiviral efficacy of Triton X-100 and TDAO decreased for the herpes viruses. Variability in inactivation was observed among the surfactants at 0.5x CMC, indicating that surfactant characteristics influence their antiviral performance below CMC. CONCLUSIONS: Adding salt enhanced the antiviral efficacy of surfactants by lowering their CMC while maintaining consistent virus inactivation. Among the surfactants tested, the glucoside with a longer tail, n-nonyl-β-D-glucoside (NG), emerged as the most robust and could function as an eco-friendly surfactant for virus inactivation in bioprocessing. For NG, virus inactivation was independent of all variables tested
Adhesion mechanisms of SBS modified asphalt mixtures: Molecular dynamics and density functional theory analysis under aging and chloride erosion
No full textThe study aims to understand the adhesion damage mechanism of SBSMA (Styrene-Butadiene-Styrene Modified Asphalt) at the interface with aggregates in complex service environments. Molecular models of SBS at various aging stages were created and assessed for their physicochemical properties, such as infrared spectra and chemical reactivity, using quantum chemical simulation methods. The accuracy of the SBSMA models was verified through parameters like density and cohesive energy density. Interface models with different mineral aggregates in pure water and salt solutions were constructed to study the adhesion characteristics under varying thermo-oxidative aging intensities, mineral types, and chloride salt concentrations. The study found that as aging intensity increases, SBS molecules experience agglomeration, chain breaking, and polarity enhancement. This leads to intensified migration and diffusion, causing competitive adsorption at the interface and a tendency for SBS molecules to be adsorbed more on the side of the aggregates. Water molecules can permeate the aggregate surface, forming a strong hydrogen bonding network and occupying numerous adsorption sites, which weakens the adhesion properties between asphalt and aggregate. However, the presence of ions disrupts this network, and ionic bridges can indirectly increase the adhesive strength between asphalt and aggregate, particularly with age. Using a calcium carbonate substrate results in ion pairs being adsorbed close to the aggregate, forming a solvated shell layer with upper water molecules, which delays the increase in adhesion energy. This study provides significant insights into composite modification and reclaimed pavements of SBSMA, highlighting the impact of aging intensity, mineral type, and chloride concentration on the adhesion properties of asphalt-aggregate interfaces
Developing a Fatigue Model for Construction Workers: An Interpretable Machine Learning Approach
No full textThe construction industry is one of the most hazardous sectors worldwide, with extremely high rates of occupational deaths and injuries. Worker fatigue, caused by undertaking physically demanding tasks in awkward working postures over prolonged daily durations, has been recognized as the main cause of these accidents. Additionally, fatigue can lead to reduced work efficiency and increased absenteeism, undermining labor productivity. This study aims to develop an accurate and reliable model to estimate the fatigue levels of construction workers. Field studies were conducted with 156 construction workers at four construction sites in mainland China. A series of physiological, personal, work-related, and environmental factors were measured and monitored to establish an interpretable machine learning model for assessing fatigue levels. The developed interpretable machine learning model exhibited good fitting with high accuracy, evidenced by the random forest model attaining an R2 value of 0.9953 through the 10-fold cross-validation method. Furthermore, this model could transparently reveal the mechanisms underlying the prediction of worker fatigue. Work duration, work session (i.e., morning session, afternoon session), environmental parameters (i.e., air temperature, humidity, wind velocity, and radiation), and worker age were identified as key factors affecting the fatigue of construction workers. The developed fatigue model can prevent excessive fatigue among construction workers, and the model interpretation results may benefit the industry by making solid guidelines and practice notes to alleviate worker fatigue
Microstructure, Processing, and Properties of Early Twentieth Century Wrought Iron
No full textWrought iron is known as a material of antiquity, with archaeological evidence of production dating back approximately 5000 years. Around the turn of the twentieth century, wrought iron was produced by a transient liquid phase processing method, known as puddling. The present work discusses the historical process of puddling to produce wrought iron and relates effects of this process on the microstructure. Microstructure analysis and mechanical testing were performed on a wrought iron, boiler stay-bolt from a steam locomotive built in the early twentieth century. State-of-the-art characterization techniques revealed the composition, anisotropic microstructure, and chemical distribution in the ferrite and slag. Mechanical testing and fracture analysis indicated anisotropic mechanical performance reflecting the microstructure and processing technology used to produce the wrought iron