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Machine Learning for Leadership in Energy and Environmental Design Credit Targeting: Project Attributes and Climate Analysis Toward Sustainability
Get PDFAchieving Leadership in Energy and Environmental Design (LEED) certification is a key objective for sustainable building projects, yet targeting LEED credit attainment remains a challenge influenced by multiple factors. This study applies machine learning (ML) models to analyze the relationship between project attributes, climate conditions, and LEED certification outcomes. A structured framework was implemented, beginning with data collection from the USGBC (LEED-certified projects) and US NCEI (climate data), followed by preprocessing steps. Three ML models—Decision Tree (DT), Support Vector Regression (SVR), and XGBoost—were evaluated, with XGBoost emerging as the most effective due to its ability to handle large datasets, manage missing values, and provide interpretable feature importance scores. The results highlight the strong influence of the LEED version and project type, demonstrating how certification criteria and project-specific characteristics shape sustainability outcomes. Additionally, climate factors, particularly cooling degree days (CDD) and precipitation (PRCP), play a crucial role in determining LEED credit attainment, underscoring the importance of regional environmental conditions. By leveraging ML techniques, this research offers a data-driven approach to optimizing sustainability strategies and enhancing the LEED certification process. These insights pave the way for more informed decision-making in green building design and policy, with future opportunities to refine predictive models for even greater accuracy and impact
First Year to Future Career: Women’s Engagement in Technical Participation Is Associated with Long-Term Retention
Get PDFSocieties realize the value of increasing the number of engineering and other STEM graduates, yet universities often struggle to enroll and retain STEM students, particularly women. To remedy this, many engineering programs have shifted their pedagogical approaches to include project-based learning in group settings. However, prior research on engineering teams revealed, for example, gender gaps in active participation, reflecting stereotypes of men as engineering experts and women as supporters. In the current study, we examined the long-term correlates of such gaps. Specifically, in a mixed-method study (behavioral observation, surveys, and longitudinal follow-up) we found gender differences in active technical participation during students’ first year in engineering project group presentations, such that men engaged in more active participation than women (N = 589). Longitudinal follow-ups in their final year revealed that first year technical participation was a predictor of feelings of belonging, and these feelings of belonging in turn predict retention in engineering majors and intentions to pursue graduate education in engineering. Together, these results suggest that the first year engineering team experience plays an important role in retaining students and highlight opportunities for early interventions
Towards Stable Biologics: Understanding co-excipient effects on hydrophobic interactions and solvent network integrity
No full textThe formulation of biologics for increased shelf life stability is a complex task that depends on the chemical composition of both the active ingredient and any excipients in solution. A large number of unique excipients are typically required to stabilize biologics. However, it is not well-known how these excipient combinations influence biologics stability. To examine these formulations at the molecular level, we performed molecular dynamics simulations of arginine - a widely used excipient with unique properties - in solution both alone and with equimolar concentrations of lysine or glutamate. We studied the effects of these mixtures on a hydrophobic polymer model to isolate excipient mechanisms on hydrophobic interactions relevant in both protein folding and aggregation, crucial phenomena in biologics stability. We observed that arginine is the most effective single excipient in stabilizing hydrophobic polymer folding, and its effectiveness is augmented by lysine or glutamate addition. We decomposed the free energy of polymer folding/unfolding to identify that the key source of arginine-lysine and arginine-glutamate synergy is a reduction in destabilizing polymer-excipient interactions. We additionally applied principles from network theory to characterize the local solvent network embedding the hydrophobic polymer. Through this approach, we found arginine supports a more highly connected and stable local solvent network than in water, lysine, or glutamate solutions. These network properties are preserved when lysine or glutamate are added to arginine solutions. Taken together, our results highlight important molecular features in excipient solutions that establish the foundation for rational formulation design
Distributional Hessian and Divdiv Complexes on Triangulation and Cohomology
No full textWe construct discrete versions of some Bernstein-Gelfand-Gelfand (BGG) complexes, i.e., the Hessian and the divdiv complexes, on triangulations in two dimensions and three dimensions. The sequences consist of finite elements with local polynomial shape functions and various types of Dirac measures on subsimplices (generalizations of currents). The construction generalizes Whitney forms (canonical conforming finite elements) for the de Rham complex and Regge calculus/finite elements for the elasticity (Riemannian deformation) complex from discrete topological and Discrete Exterior Calculus perspectives. We show that the cohomology of the resulting complexes is isomorphic to the continuous versions, and thus isomorphic to the de Rham cohomology with coefficients
MicroCT and contrast-enhanced microCT to study the in vivo degradation behavior and biocompatibility of candidate metallic intravascular stent materials
No full textBiodegradable intravascular stents offer a promising alternative to permanent stents for treating atherosclerosis-related artery narrowing by potentially avoiding long-term complications. Identifying materials that degrade harmlessly and uniformly at a suitable rate is crucial. This study evaluated an advanced zinc alloy (Zn-Ag-Cu-Mn-Zr) alongside pure iron and pure zinc, using a simplified stent model of metallic wires implanted in the rat aorta. Assessments were made at 7, 24, and 84 days post-implantation using X-ray microfocus computed tomography (microCT) and contrast-enhanced microCT (CECT). For CECT, a contrast agent was chosen to provide optimal soft tissue contrast and minimal interaction with the wires. This combination of imaging techniques allowed us to evaluate degradation behavior, compare volume loss in various locations (outside the arterial lumen, inside the lumen, and encapsulated by neointima), compute degradation rates, and evaluate neointima tissue formation. Results showed that zinc and its alloy degrade less uniformly than iron, which demonstrates uniform surface degradation. The zinc alloy had a higher initial volume loss than the other materials but showed little increase over time. Neointima formation was similar for zinc and the zinc alloy, while iron provoked less tissue formation than both zinc and the reference cobalt-chromium alloy. Additionally, unlike cobalt-chromium and zinc, iron wires did not achieve consistent tissue encapsulation along their entire length, which may impair their performance. Mild inflammation was noted around zinc-based implants. Combining microCT and CECT provided 3D information on degradation uniformity, degradation products, and neointima morphometrics, highlighting the power of these imaging techniques to evaluate implant materials in a highly accurate way compared to previous 2D methods. Statement of significance: Biodegradable intravascular stents offer a promising solution to long-term complications associated with permanent stents by gradually dissolving in the body. To evaluate a novel zinc alloy (Zn-Ag-Cu-Mn-Zr) with improved mechanical properties, microstructure, and biocompatibility, we compared it to pure iron and zinc. We used advanced 3D imaging techniques, i.e., microCT and contrast-enhanced microCT, to assess the degradation behavior and the tissue response in a rat aorta model. The zinc alloy demonstrated promising properties despite less uniform degradation and mild inflammation compared to iron. Our findings highlight the superiority of 3D imaging over previously used 2D techniques in evaluating stent materials, offering critical insights into degradation processes and biocompatibility. These highly accurate measurements provide crucial information for developing improved biodegradable implants
Unveiling novel structural complexity of spiral carbon nanomaterials: Review on mechanical, thermal, and interfacial behaviors via molecular dynamics
No full textNanotechnology has been extensively studied for its potential to effectively control material properties. Recent research has shown a growing interest in nanomaterials and the development of modeling systems and manufacturing processes. Carbon-based materials, known for their exceptional mechanical, thermal, and electrical properties, have emerged as promising candidates for nanodevices and nanocomposites. Among carbon-based nanomaterials, spiral carbon-based nanomaterials (SCBNs) have drawn attention due to their unique geometry and carbon properties. This group includes coiled carbon nanotubes (CCNTs), graphene helicoids (GHs), and conical coiled carbon nanotubes. By manipulating their geometrical parameters, functionalization, chemical doping, and mechanical loadings, a wide range of features can be achieved in these nanostructures. Furthermore, their distinct geometry allows for unique interactions with their surroundings. This review focuses on the research conducted using molecular dynamics to investigate the mechanical, thermal, and interfacial properties of SCBNs. Different factors were taken into consideration by researchers, such as tube diameter, temperature, durability, and incorporation into composites, among others. The insights gained from these studies contribute to a better understanding of SCBN characteristics, which is essential for controlling their properties in nanodevices and nanocomposites
Multiscale revelation of asphalt morphology and adhesion performance evolution during stress relaxation process
No full textThe objective of this study is to investigate the evolution of asphalt morphology and adhesion properties during stress relaxation using atomic force microscopy (AFM) and molecular dynamics (MD) simulations. Changes in surface roughness, Derjaguin-Muller-Toporov (DMT) modulus, adhesion force, and force-distance curves were analyzed. Morphological and adhesion property evolution was assessed using correlation length and Lyapunov exponent. MD simulations provided insights into the internal stress-time relationship and free volume fraction of asphalt during relaxation. Results indicate that under constant tensile strain, the “bee structure” on the asphalt surface elongates, dark pits transform into light protrusions, microcracks heal, and surface roughness stabilizes after 10 h. Under compressive strain, similar changes occur but are more pronounced during tensile relaxation. The combined use of AFM and MD simulations offers a comprehensive understanding of the microscopic evolution mechanisms of asphalt under stress relaxation, providing valuable insights for optimizing asphalt pavement design and maintenance
A global dataset of nitrogen fixation rates across inland and coastal waters
Get PDFBiological nitrogen fixation is the conversion of dinitrogen (N2) gas into bioavailable nitrogen by microorganisms with consequences for primary production, ecosystem function, and global climate. Here we present a compiled dataset of 4793 nitrogen fixation (N2-fixation) rates measured in the water column and benthos of inland and coastal systems via the acetylene reduction assay, 15N2 labeling, or N2/Ar technique. While the data are distributed across seven continents, most observations (88%) are from the northern hemisphere. 15N2 labeling accounted for 67% of water column measurements, while the acetylene reduction assay accounted for 81% of benthic N2-fixation observations. Dataset median area-, volume-, and mass-normalized N2-fixation rates are 7.1 μmol N2-N m−2 h−1, 2.3 × 10−4 μmol N2-N L−1 h−1, and 4.8 × 10−4 μmol N2-N g−1 h−1, respectively. This dataset will facilitate future efforts to study and scale N2-fixation contributions across inland and coastal aquatic environments
Progress in the development of NiO/MgO solid solution catalysts: A review
No full textNiO/MgO solid solution materials have emerged as highly effective catalysts for various catalytic processes, including dry methane reforming, CO2 hydrogenation, methane partial oxidation, and steam reforming of hydrocarbons. The similar lattice parameters of NiO and MgO allow the formation of homogeneous solid solutions, where metallic Ni particles can be generated through the NiO reduction that is controlled by MgO isolation effect on NiO in the solid solution. The small size of these particles plays a crucial role in preventing carbon deposition and sintering in catalytic reactions. In this review, the basic principles of the formation of NiO/MgO solid solution, the reduction properties of the catalysts, and the insight into its high catalytic activity are elucidated. The synthesis methods of NiO/MgO solid solutions are presented. In addition, the recent progress of catalytic applications of NiO/MgO solid solutions is provided
Evaluating Geomechanical Uncertainty and Slope Reliability Analysis in Open Pit Mine Planning and Optimization
No full textOpen pit mine Queryplanning involves numerous uncertainties, with geomechanical uncertainty being one of the most critical factors for ensuring safe operations. Slope stability analysis plays a pivotal role in pit optimization, as slope angles directly influence both safety and profitability. However, determining a stable slope angle with certainty is challenging due to limited geomechanical data. This study integrates geomechanical uncertainty into pit optimization through a reliability-based slope stability analysis. Using stochastic modeling, this research evaluates various slope angles for an open pit gold deposit in Alaska, utilizing Rock Quality Designation (RQD) as the primary geomechanical input. The uncertainty of RQD was quantified by developing theoretical probability density function (PDF) of four rock types of the study mine and used to estimate cohesion, internal friction angle, and rock unit weights. Slope stability was assessed using Monte Carlo simulations within the limit equilibrium method, accounting for parameter variability. For slope angles up to 48°, the probability of failure was 0%, ensuring 100% reliability, and generating a cash flow of 2.935 billion at 56°, but a failure probability of 23% and reduced reliability to 77%. This demonstrates a clear trade-off between maximizing cash flow and managing slope stability risk. For a conservative design, a 48° slope is recommended, balancing safety and profitability. However, risk-tolerant operations may consider steeper slopes, accepting increased failure probability for higher profits, with a 1° slope increase adding $26 million but raising the failure risk by 12%