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Fabricating three-dimensional metamaterials using additive manufacturing: An overview
No full textClick on the DOI link to access this review at the publishers website (may not be free).Metamaterials are artificial materials composed of special microstructures that have properties with unusual and useful features and can be applied to many fields. With their unique properties and sensitivity to external stimuli, metamaterials offer design flexibility to users. Traditional manufacturing is often not up to the task of creating metamaterials, which are now more accurately and more effectively analyzed than they were in the past. Recent advances in additive manufacturing (AM) have achieved remarkable success, with ensemble machine learning models demonstrating R2 values exceeding 0.97 and accuracy improvements of 9.6% over individual approaches. State-of-the-art multiphoton polymerization (MPP) techniques now reach submicron resolution (<1 ?m), while selective laser melting (SLM) processes provide 20-100 ?m precision for metallic metamaterials. This work offers a comprehensive review of additively manufactured 3D metamaterials, focusing on three categories of their fabrication: electromagnetic (achieving bandgaps up to 470 GHz), acoustic (providing 90% sound suppression at targeted frequencies), and mechanical (demonstrating Poisson's ratios from ?0.8 to +0.8). The relationship between different types of AM processes used in creating 3D objects and the properties of the resulting materials has been systematically reviewed. This research aims to address gaps and develop new applications to meet the modern demand for the broader use of metamaterials in advanced devices and systems that require high efficiency for sophisticated, high-performance applications
Investigating speech enhancement towards robust synthetic audio spoofing detection in the wild
No full textPoster project completed at Wichita State University, School of Computing and INRS-Canada, Department of Computer SciencePresented at the 22nd Annual Capitol Graduate Research Summit, Topeka, KS, March 25, 2025.Logical Access (LA) attacks involve the use of Text-to- Speech (TTS) or voice conversion (VC) techniques to generate spoofed speech data. This represents a serious threat to automatic speaker verification as intruders can use such attacks to bypass biometric security systems. In this study, we train a state-of-the-art model to distinguish between bonafide and spoofed speech samples, and we investigate its performance in the wild. For that, we used the LA data provided in the ASVspoof 2019 Challenge in the presence of different levels and types of background noises. We also explored two enhancement algorithms, namely SEGAN and MetricGAN+, to mitigate the detrimental effects of noisy speech. Results show that applying enhancement priorto the LA task can improve performance in more degraded scenarios. We also found that quality measures, such as PESQ, can be an important asset as indicator of enhancement algorithms performance
Introduction
No full textClick on the DOI link to access this article at the publishers website (may not be free).[No abstract available
Neural network-based analysis of mammography images for identifying breast cancer histological subtypes
No full textClick on the DOI link to access this article at the publishers website (may not be free).Breast cancer (BC) is a complex disease with multiple histological subtypes that exhibits distinct biological and clinical characteristics. Accurate identification of these subtypes is crucial for the implementation of personalized treatment strategies and the improvement of patient outcomes. This study aimed to leverage the potential of neural networks and mammography data to identify specific histological subtypes of BC, with a focus on distinguishing between in situ and invasive carcinoma. We used the Digital Database for Screening Mammography (DDSM) and its curated subset, CBIS-DDSM, which provides mammography images of normal and malignant cases with verified pathological information. A convolutional neural network (CNN) architecture was designed to extract relevant features from mammography images. The model was trained using data augmentation techniques to enhance diversity and mitigate overfitting, resulting in training and validation accuracies ranging from 0.98 to 1.0. The features extracted by the trained model were then used for clustering analysis using Kmeans on PCA (KM-PCA), which identified nine well-separated clusters with minimal overlap. The clustering results were validated using a subset of mammography images with confirmed invasive carcinoma types from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) dataset. Two validation approaches were incorporated to strengthen the hypothesis and further validate the results. Overall, this study demonstrates the potential of combining neural networks and mammography data to accurately identify BC histological subtypes, which could pave the way for personalized and effective treatment strategies. © 2024 IEEE.Charles Sturt University; et al.; IEEE; Melbourne Institute of Technology; University of Notre Dame Australia; Western Sydney Universit
Farmers’ attitude towards green ammonia produced by upcycling waste nitrogen: Empirical evidence from an Iowa study
No full textThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).This study examines farmers' acceptance of green ammonia produced by upcycling waste nitrogen using renewable energy. A mail survey, targeting a random sample of crop growers in Iowa, USA, found moderately high acceptance: about 50 % support green ammonia as a fertilizer and 32 % support green ammonia as a fuel. Support for green hydrogen is only 17 % (24 % opposing), demonstrating a preference of the 2nd-generation over the 1st-generation technologies. Ordinal logistic regression reveals social and psychological factors affecting attitude, including income, ideology, perceived benefit, ammonia usage, trust in science and technology, personal belief in reducing waste nitrogen, and social norm. © 2025 The Author(s
On the design and application of offline frameworks for investigating the accessibility of indoor spaces for individuals with disabilities
No full textThesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of Electrical and Computer EngineeringPersons with disabilities (PWDs) face significant challenges when navigating unfamiliar indoor spaces due to physical obstacles such as narrow doorways, uneven flooring, and obstructed pathways, as well as limited signage and the absence of satellite-based positioning systems indoors. These barriers hinder independent mobility and are often compounded by the lack of scalable assistive technologies, particularly during early design phases when physical testing is impractical. Recruiting participants for real-world mobility studies can also be logistically challenging and time-consuming. This dissertation addresses these limitations by developing offline, simulation-based and analytical tools that enable early-stage evaluation of spatial accessibility without requiring live user testing. Two key contributions are introduced: MABLESim, a simulation framework that generates interactive 3D environments from 2D architectural plans to model diverse user navigation scenarios; and AccessQuotient, a graph-based metric that quantifies route usability based on layout complexity, decision points, and predicted wayfinding success. MABLESim supports both standardized simulations and customized, user-defined scenarios, helping identify design flaws that may not be apparent through conventional methods. AccessQuotient complements this by producing interpretable accessibility scores that can inform layout optimization. Together, these tools provide a scalable, repeatable, and user-centered approach to accessibility assessment. By reducing reliance on physical testing and providing actionable insights into spatial usability, this dissertation advances inclusive design practices and equips architects, planners, and accessibility professionals with methods to create more navigable and human-centered indoor environments from
the earliest stages of planning
Compact integration of NV-based diamond quantum sensors using a small-size photodiode and on-board transimpedance amplifier
No full textThesis (M.S.)-- Wichita State University, College of Liberal Arts and Sciences, Dept. of Mathematics, Statistics, and PhysicsThis thesis presents the development and characterization of a miniature NV-based diamond magnetometer for room-temperature magnetic field sensing, which combines an on-board transimpedance amplifier (TIA) and a high-responsivity photodiode. Using optically detected magnetic resonance (ODMR) in nitrogen-vacancy (NV) centres, Photoluminescence from NV is converted into voltage signals for magnetic field analysis.
Using a PDB-C171SM photodiode with a quantum efficiency of 0.65 and a responsivity of 0.25 A/W, a photocurrent of roughly 0.74 μA was generated at an optical power of 4.50 nW. The TIA achieved a gain of 59.2 MΩ and a -3 dB bandwidth of approximately 18 Hz when it was set up with a 50 MΩ feedback resistor and a 560 pF capacitor. ODMR detected the zero-field resonance at 2868.76 MHz , and Zeeman splitting in an external magnetic field produced a frequency shift of 213.65 MHz, corresponding to a field strength of approximately 38.15 G.
Based on shot-noise-limited performance, the estimated magnetic field sensitivity was 26.53 nT/ with the photon detection rate of 1.44 × 10¹⁰ photons/s. These results validate the sensor’s capability for miniaturized, high-resolution, and low-power absolute magnetic field detection using NV centers
National survey of 4th and 5th grade science education teachers: Insights into instruction and inclusion of students with disabilities
No full textThis is an open access article under the CC BY license.Elementary science education, particularly in the 4th and 5th grades, is essential for setting the foundation for lifelong science learning, fostering critical thinking, and preparing students for success in science, technology, engineering, and mathematics (STEM) fields. This stage is especially critical for students with disabilities, as achievement gaps between them and their peers emerge during elementary school. Despite this importance, little is known about how science is taught in elementary classrooms during these critical years, particularly for students with disabilities. To address this gap, we surveyed teachers from a nationally representative sample of U.S. schools to examine elementary science education, including instructional practices, allocation of time, and the inclusion and support of students with disabilities. Our findings reveal that limited instructional time is allocated to science, with significant variability across classrooms. The amount of time dedicated to science instruction was significantly influenced by external factors, such as whether science was a tested subject. Students with disabilities often face additional barriers, including being pulled out of science instruction for special education services, resulting in missed opportunities to engage in science. These findings highlight the need to address opportunity gaps in science instruction to ensure all students have meaningful access to quality science education. © 2025 The Author(s). Science Education published by Wiley Periodicals LLC.National Science Foundation, NSF, (2201464); National Science Foundation, NSFThis material is based upon work supported by the National Science Foundation under Grant No. 2201464. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. This paper is a product of the Special Education Research Accelerator (SERA), which leverages crowdsourcing to conduct high\u2010quality studies in special education. For more information about SERA, please visit https://edresearchaccelerator.org/
Using public–private partnerships for political reasons: the Government’s motivation and conditions
No full textClick on the DOI link to access this article at the publishers website (may not be free).Why does government use Public–Private Partnerships (PPPs)? Besides their economic values, we argue PPPs are used for political reasons. Employing a political agency framework, we explain how politicians use PPPs to appear competent and extract rents, and under what conditions these political potentials are attractive and accessible to politicians. By analysing U.S. state-level data, we reveal some political and fiscal variables that are associated with PPP initiations and uncover state PPP legal framework diminishes the associations. By echoing literature on public administration, political economy and state politics, our research responds to the call to study the political natures of PPPs. © 2025 Informa UK Limited, trading as Taylor & Francis Group