Virginia Tech - Wake Forest University School of Biomedical Engineering & Sciences
VTech Works (Virginia Tech)Not a member yet
122049 research outputs found
Sort by
Computers
No full textAgriculture uses most of the world’s fresh water. Given that the worldwide population is expanding at an alarming rate, more land cultivation is apparently in demand. As a result, much more water would be required to irrigate cultivable lands. However, fresh water is becoming scarce at a faster rate due to climate uncertainties and over-exploitation. Several controlled irrigation techniques, such as drip and sprinkler irrigation, have been introduced to safeguard water resources. However, these techniques do not readily meet crop water demands and often end up causing overapplication of water. Under these circumstances, smart precision irrigation is the best solution. Smart irrigation techniques facilitate delivery of water in an amount that is required by the crop as per site/location and temporal requirements. Several studies have been carried out in this area, and remarkable progress has been observed. These studies range from making use of in situ sophisticated sensors that are low-cost and consume minimum energy up to the use of small unmanned aerial systems (SUAS) and satellite imagery for irrigation management. This review summarizes research studies that highlight the components of developing and deploying various precision irrigation technologies, their benefits, and their limitations. Specifically, the scientific value of this study lies in outlining implications of using different sensors, parameters, and equipment, the agroclimatic models, communication technologies, artificial intelligence, and the energy sources to implement automated irrigation systems. A future scope of precision irrigation is also discussed in accordance with cost-effectiveness and sustainability. This study should also act as a referring guideline for new researchers as well as technology manufacturers who seek to design and develop a futuristic yet efficient irrigation system. Overall, this review is aimed at contributing to the understanding of automated irrigation systems for their effective deployment towards enhanced agricultural production, conserved water resources, and sustainable use of energy sources.Published versio
Quantifying the Impact of Pavement Surface Properties on Road Safety: A Data-Driven Approach
No full textThe Highway Safety Manual's (HSM) safety performance functions (SPFs) are widely used for network screening and project prioritization; however, most formulations focus on volumetric exposure and geometry and omit pavement surface characteristics that govern tire–road interaction. Despite the growing availability of network level surface data such as Skid Friction Number at 40 mph (SFN40), Macrotexture via Mean Profile Depth (MPD), and pavement age and classification, there incremental value within SPFs remain under quantified. This gap leaves agencies uncertain about surface measures materially improving prediction and how large their effects are in practice. This study addresses the established gap by quantifying how adding SFN40, MPD, and Age can affect a model's crash prediction across HSM Functional classifications. A network level dataset of 0.1-mile roadway segments was assembled across selected HSM functional classes, N = 12,474; 14 classes, linking reported crashes to exposure (lnAADT), roadway geometry, and pavement surface measurements. For each class, Negative Binomial SPFs with a log link were estimated: a base specification (lnAADT, Curvature, Cross-Slope, Grade) and an Extended specification that adds SFN40, MPD, and age. Model performance was evaluated using AIC, log-likelihood, RMSE, and Dispersion. Effects are reported as incidence rate ratios (IRR) with 95% confidence intervals, and residual structure was screened using cumulative residual (CURE) plots alongside a simple multicollinearity check.
Across 14 functional classes, the extended model outperformed the base in 12 classes, indicated by the difference in AIC median = -2.38, median difference in RMSE% = -2.7% (improved in all 14; range -26.4 % to -0.1%) and significantly LRT in 8 classes. Difference in AIC favored the Extended model in 8 classes (equal in 2 classes, higher in 4). Routine friction/texture measures and pavement age provide measurable predictive gains and should be incorporated into SPF calibration and network screening, with class specific effect sizes guiding surface focused maintenance prioritization.
Adding friction/texture to SPFs improved fit where maneuver demand concentrates. Urban arterial intersections (ΔAIC −115.9; RMSE −5.5%) and rural multilane intersections (ΔAIC −61.9; RMSE −2.3%) showed the largest gains; freeway tangents improved modestly (ΔAIC −36.7; RMSE −0.34%). Curves and rural tangents saw negligible benefits, supporting parsimony. Coefficients are reported as IRRs; SFN40 is consistently protective (≈7%, 5%, and 1% lower expected crashes per +1 point at urban intersections, rural multilane intersections, and freeway tangents, conditional on exposure and geometry). These results support selective inclusion of surface variables in agency SPFs.Master of ScienceVehicular accidents are partly random due to human interaction, but they also follow patterns we can measure. This thesis uses statistical models to test whether pavement surface conditions, how much a tire grips the road (friction) and how rough the surface is (macrotexture), help explain and predict where crashes happen beyond traffic and road design alone. Using over 12,000 0.1-mile segments in Virginia, a base model using traffic and geometry and an extended model which adds on roadway surface measurements collected by modern friction testing.
Results show that adding surface information improves prediction in most places with heavy braking and turning especially in urban and rural multilane intersections and provides smaller but measurable gains on high-speed freeway segments. In a small low sample curve category, simpler models remained the better choice, underscoring that more variables aren't always helpful.
The practical takeaway is straightforward: agencies can use routinely collected surface data to sharpen safety screening and target treatments (like resurfacing or friction restoration) where they will prevent the most crashes. Where the gains are small, agencies can keep a simpler approach. By identifying when surface data adds value, this work helps direct limited safety and maintenance funds to locations where they can do the best
Education Science
No full textTo understand virtual leaders’ work at the intersection of equity and community, virtual school leadership (VSL) was examined with relevance to preparation and research. Research questions were: How is VSL described in extant literature? How is VSL applicable to leaders’ preparation and development? An integrative review approach was applied to online learning and virtual leadership linked to community and equity concepts. Document analysis was used to qualitatively code 34 (of 132) studies. Despite the demand for cyber schooling, some US preservice programs may lack training on leading equitably and collaboratively in virtual environments. Five findings address what virtual school leaders (aspire to) do in their jobs. Community and equity were leadership orientations as well as concerns discerned from perceptions of virtual schooling. Online public education is ensnared in global democratic backsliding for 82 countries, yet VSL remains underexplored in research. This literature review/conceptual work introduces Equity and Community in K–12 Online Leadership, an original conceptual framework informed by professional standards, virtual learning theories, and factors central to leadership. A critique of findings, along with recommendations for leadership preparation and practice, responds to the call for better preparing preservice leaders for the demands of K–12 online learning.Published versio
Optics Express
No full textDiffractive optic elements offer significant advantages in optical system design, enabling lightweight and compact architectures compared with conventional refractive and reflective components. However, accurately modeling wave-optical effects in such systems remains challenging because characteristic wavelengths of light are much smaller than the overall dimensions of typical optical assemblies. Conventional ray-tracing methods generally neglect these effects, while full-wave simulations become computationally prohibitive for large-scale systems. To overcome these limitations, we introduce a numerical implementation of the Monte Carlo ray-tracing approach based on the Huygens–Fresnel principle to predict key optical parameters, including focusing efficiency, focal spot size, and diffraction patterns with high fidelity. This approach is validated through systematic comparisons of dedicated experimental, theoretical, and numerical results, demonstrating accurate performance over a broad range of optical configurations. We further demonstrate that photon sieves incorporating large numbers of pinholes distributed across Fresnel zones can focus light into spots smaller than the smallest pinhole diameter while strongly suppressing higher diffractive orders and sidelobes. These results highlight the potential of the ray-tracing approach as a practical tool for both the design and optimization of next-generation diffractive optical elements.Published versio
Data Plus
No full textThe accelerating climate crisis presents an urgent global challenge that demands effective leadership across all levels of society. This paper argues that global leadership, characterised by vision, inclusivity, collaboration, and innovation, is essential for advancing sustainable development and responding effectively to climate change. Drawing on the Global Leadership for Sustainability (GLfS) model, this paper examines how leadership grounded in ethics, interconnectedness, and systemic thinking can bridge the gap between science and policy. The paper highlights how diverse leadership approaches catalyze tangible climate action using successful case studies such as the Montreal Protocol, the International Solar Alliance, and youth-led climate movements. It also examines persistent barriers, including misinformation, political polarization, economic inequality, and cultural divides, that hinder unified global efforts. The analysis reveals that overcoming these challenges requires empowering communities, enhancing climate financing, fostering international cooperation, and cultivating emerging youth leaders. This calls on global leaders to lead by example, engage the private sector, and prioritize equity in decision-making. Ultimately, it positions leadership not merely as a function of governance but as a moral obligation to ensure planetary survival. Adopting a global leadership mindset can help humanity forge a resilient and sustainable future amid the climate crisis.Published versio
Angewandte Chemie International Edition
No full textWe report herein the development of catalytic asymmetric synthesis of secondary alkylboronates. Under the optimal conditions, Cu-catalyzed semi-reduction of 1-alkyl- or 1,3-dialkyl-substituted 1-boryl-1,3-butadienes forms secondary alkylboronates with excellent regioselectivities and enantioselectivities. With H2O as the source of hydrogen, the reaction proceeds through a protoboration and protodeboration cascade reaction sequence to generate the desired boronates. By using a slightly modified protocol, the process allows for access to enantioenriched deuterium-labeled secondary alkylboronates. Density functional theory (DFT) studies were conducted to probe the origins of selectivities.Published versio
Likelihood-Free Bayesian Inference with Efficient Uncertainty Quantification
No full textUncertainty quantification (UQ) in inverse problems is essential for reliable parameter estimation in scientific and engineering applications. This thesis presents a study on two frameworks that separately quantifies two fundamental types of uncertainty: aleatoric uncertainty, arising from inherent measurement noise and non-identifiability in the inverse mapping, and epistemic uncertainty, stemming from limited training data and model inadequacy.
For aleatoric uncertainty quantification, a conditional Wasserstein Generative Adversarial Network with Full Gradient Penalty (cWGAN-GP) is employed to approximate the posterior distribution over parameters given observations. The trained generator enables efficient posterior sampling through a single forward pass, providing credible intervals and capturing potential multimodality in the solution space. A physics-informed extension, SGML-cWGAN, incorporates domain knowledge through physics-based loss terms to improve estimation accuracy. For epistemic uncertainty quantification, Prediction with Neural Network Corrections (PNC) is utilized, leveraging Neural Tangent Kernel theory to provide theoretically grounded uncertainty estimates. Bootstrap and stacking resampling methods generate multiple model instances, with prediction variance across instances serving as the epistemic uncertainty measure. The framework is evaluated on two benchmark problems: the FitzHugh-Nagumo (FHN) dynamical system and the Pacejka tire model. Results demonstrate that PNC achieves excellent performance on clean and structured noisy datasets, while cWGAN scales efficiently to large datasets containing up to 864,000 samples. The physics informed SGML-cWGAN achieves up to 33% improvement in mean squared error over the baseline cWGAN on the Pacejka dataset. However, a fundamental trade-off emerges: PNC faces computational constraints limiting applicability to datasets smaller than approximately 7,000 samples, while cWGAN requires a minimum of 8,000 samples for reliable performance. This incompatibility highlights the need for scalable epistemic uncertainty methods that complement data-hungry generative models. The findings demonstrate the viability of neural network-based approaches for uncertainty quantification in inverse problems, while identifying key limitations and directions for future research, including alternative simulation-based inference methods and improved posterior evaluation metricsMaster of ScienceMany scientific and engineering problems require estimating unknown parameters from measured data under a process called an inverse problem. A critical challenge in these problems is understanding how confident we can be in our estimates: Are the measurements precise enough to pinpoint a unique answer, or could multiple parameter values explain the data equally well? This thesis study two computational frameworks to quantify two types of uncertainty in inverse problems. The first type, called aleatoric uncertainty, represents the fundamental ambiguity that exists even with perfect methods—some inverse problems simply have multiple valid solutions, or measurement noise makes precise estimation impossible. The second type, called epistemic uncertainty, represents uncertainty due to limited knowledge—having more data or better models would reduce this uncertainty. To capture aleatoric uncertainty, this work employs a type of neural network called a Generative Adversarial Network (GAN), which learns to generate plausible parameter values that could have produced the observed measurements. Rather than providing a single "best guess," this approach produces a range of possibilities along with their relative likelihoods. For epistemic uncertainty, a different technique called Prediction with Neural Network Corrections (PNC) is used, which estimates how much predictions might change if different training data were available. The framework was tested on two applications: a mathematical model of nerve cell behavior (the FitzHugh-Nagumo model) and a model used in automotive engineering to describe tireroad interactions (the Pacejka model). Results show that both methods successfully quantify uncertainty when meaningful patterns exist in the data, while appropriately indicating high uncertainty when analyzing pure noise. A physics-informed version for the Pacejka data that incorporates known physical laws achieved be
Enhancing English Language Learning Skills by Using Metaverse Technology: An Integrative Literature Review
No full textTechnology has been developing in ways that can help students learn better, including how they learn languages such as English. The purpose of this study was to analyze prior research on the use of Metaverse Technology (MvT) in educational settings, focusing on studies centered on English as a Foreign Language (EFL) students, for the purpose of enhancing English Language Learning (ELL) skills and formulating guidelines for instructors in Higher Education Institutions (HEIs). The potential of MvT in EFL is that students can practice the English language inside digital spaces that feel real, such as talking, solving problems, or working on group projects together. They can talk, move, and solve problems inside those spaces instead of only reading or listening in a traditional class. This study utilized an integrative literature review (ILR) approach related to how instructors use MvT to enhance EFL skills among students. Further, the study identified how the integration of MvT could address challenges in engaging students and improving their English Language skill proficiencies. The objective of the study was to identify practical, evidence-based ideas that instructors could use to improve student learning. The process involved completing an integrative literature review, which was screened, compared, and grouped by shared themes. The results of this study contribute to instructional design (ID) research, suggesting practical ways that universities and instructors may incorporate MtVs into EFL in Higher Education.Doctor of PhilosophyAlthough English is the language most often used in school and work around the world, many college students in Saudi Arabia consider it challenging to learn. This study looked at whether a new digital tool called Metaverse Technology (MvT) could make it easier for English as a Foreign Language (EFL) students in Higher Education Institutions (HEIs) to enhance their English Language Learning (ELL) skills. The researcher reviewed multiple articles from the past several years, illuminating how MtVs work and the ways that they are effective in teaching college students EFL. Most studies indicated that MvT helped make students more confident and less anxious when learning ELL skills. Instructors also supported the use of the technology, believing that it allowed EFL students to interact in real-life situations online, which provided students with more chances to use the language in ELL. The studies have also highlighted that an instructor's training remains crucial for ensuring reliability and success. Instructors also need to plan how MvT will be integrated into courses for EFL students, focusing on ELL skills. When used carefully, MvT can give EFL students a stronger voice in the classroom and help them learn through doing, not just through study. For the HEIs trying to meet new goals for ELL, this approach offers a practical and realistic path forward
Exploration of a new manufacturing process for improving mechanical properties in a hot extruded Al-Mg-Si alloy
No full textAl-Mg-Si alloys are age-hardenable and exhibit a good strength-to-weight ratio, formability, corrosion resistance, making them widely used in commercial applications. In particular, Al-Mg-Si alloys with high strength and elongation are preferred in the automotive industry. Components such as body sheets and bumper beams are typically manufactured by rolling or extrusion of billets, followed by artificial aging for strengthening.
In conventional processing routes, billets are produced by casting prior to subsequent deformation. However, the cooling rates during casting and homogenization treatment processes are relatively slow. Such slow solidification conditions often promote the formation of coarse (microscale) secondary phases, such as intermetallic compounds and precipitates, which negatively affect the mechanical properties, particularly strength and ductility.
To address this, the present study explores the use of laser powder bed fusion (LPBF), an additive manufacturing method, to fabricate billets for subsequent hot-extrusion. LPBF enables rapid heating and cooling of a material over a very short period. This rapid solidification can effectively suppress the formation of coarse secondary phases, thereby improving age-hardenability and enhancing both strength and ductility.
This research work discusses insights into the mechanical property as well as the macro- and microstructural characteristics of a hot-extruded Al-Mg-Si alloy using an LPBF-fabricated billet.
Compared with conventionally extruded aluminum, the material produced via the proposed LPBF-based route exhibited higher tensile strength and yield strength in the longitudinal direction while maintaining comparable ductility. Furthermore, the extruded alloy processed from the LPBF billet showed a finer grain size, which contributed to its enhanced strength relative to the conventionally extruded counterpart.Master of ScienceAluminum-magnesium-silicon (Al-Mg-Si) alloys are lightweight, strong, and resistant to corrosion, making them popular in industries like automotive manufacturing. Traditionally, components such as car body panels and bumper beams are made by casting aluminum billets, shaping them through rolling or extrusion, and then strengthening them through aging. However, the slow cooling in conventional casting can lead to the formation of coarse internal structures, which reduce strength and ductility.
This study explores a new approach using laser powder bed fusion (LPBF), a type of 3D printing, to make aluminum billets before hot extrusion. LPBF rapidly heats and cools the material, helping prevent the formation of coarse structures and improving both strength and ductility.
The results show that the hot-extruded alloy made from LPBF billets has higher tensile and yield strength compared with conventionally produced aluminum, while keeping similar flexibility. The material also has a finer grain structure, which contributes to its improved mechanical properties. These findings suggest that using LPBF to produce billets could be a promising method to make stronger and more reliable aluminum components
Journal of Composites Science
No full textThis paper presents a novel carbon fiber reinforced polymer (CFRP) crash box design, incorporating numerical analysis and manufacturing aspects. Within the design and analysis phases, a novel numerical methodology is employed to mitigate computational costs in estimating specific energy absorption (SEA). The proposed approach involves a reduction in ply interfaces and modification of pertinent material properties to optimize energy dissipation, achieving more than 50% reduction in simulation time. This methodology is applied to the design of a composite crash box made of unidirectional (UD) carbon/epoxy prepregs, resulting in a new geometry: sun-like shape featuring four sinusoidal arms connected to a central circular core. Subsequent manufacturing and testing reveal a SEA value of 79.46 J/g for designed geometry, surpassing metallic counterparts by a factor of 3 to 4. Furthermore, this study conducts a comparative analysis of energy absorption performance between unidirectional and woven fabric prepregs for the same geometry. Utilizing carbon/epoxy woven fabric (WF) prepregs further enhances the SEA to 89.26 J/g. Finally, the application of edge tapering to the crash box structure is shown to eliminate initial peak loads, thereby preventing excessive deceleration.Published versio