Michigan Technological University
  • Houghton, Michigan, United States
Recent publications
Timely and accurate extraction of pavement crack information is crucial to maintain service conditions and structural safety for infrastructures and reduce further road maintenance costs. Currently, deep learning techniques for automated pavement crack detection are far superior to traditional manual approaches in both speed and accuracy. However, existing deep learning models may easily lose crack details when processing images containing complex background textures or other noises. Although many studies have alleviated this challenge by introducing attention mechanisms, especially the non‐local (NL) block, which has the ability to efficiently capture long‐range dependencies to facilitate crack pixel capture, the huge computational cost of NL makes the inference time of the model too long, which is not conducive to practical implementation. In this study, a new module, namely, the pyramid region attention module (PRAM), was developed by combining the pyramid pooling module in the pyramid scene parsing network and optimized NL, which can achieve global multi‐scale context integration and long‐range dependencies capture at a relatively lower computational cost. By applying PRAM to deep skip connections in the modified U‐Net, an effective crack segmentation model called CrackResU‐Net was developed. The test results on the existing CrackForest dataset showed that CrackResU‐Net not only achieved an F1 score of 0.9580 but also took only 25.89 ms to process an image with a resolution of 480 × 320, which had advantages in accuracy and speed, compared with several other state‐of‐the‐art crack segmentation approaches. It was fully demonstrated that this approach could realize automatic fast and high‐precision recognition of pavement cracks for engineering purposes.
Previous investigators have developed prediction equations to estimate arterial occlusion pressure (AOP) for blood flow restriction (BFR) exercise. Most equations have not been validated and are designed for use with expensive cuff systems. Thus, their implementation is limited for practitioners. To develop and validate an equation to predict AOP in the lower limbs when applying an 18 cm wide thigh sphygmomanometer (SPHYG18cm). Healthy adults (n = 143) underwent measures of thigh circumference (TC), skinfold thickness (ST), and estimated muscle cross-sectional area (CSA) along with brachial and femoral systolic (SBP) and diastolic (DBP) blood pressure. Lower-limb AOP was assessed in a seated position at the posterior tibial artery (Doppler ultrasound) using a SPHYG18cm. Hierarchical linear regression models were used to determine predictors of AOP. The best set of predictors was used to construct a prediction equation to estimate AOP. Performance of the equation was evaluated and internally validated using bootstrap resampling. Models containing measures of either TC or thigh composition (ST and CSA) paired with brachial blood pressures explained the most variability in AOP (54%) with brachial SBP accounting for majority of explained variability. A prediction equation including TC, brachial SBP, and age showed good predictability (R2 = 0.54, RMSE = 7.18 mmHg) and excellent calibration. Mean difference between observed and predicted values was 0.0 mmHg and 95% Limits of Agreement were ± 18.35 mmHg. Internal validation revealed small differences between apparent and optimism adjusted performance measures, suggesting good generalizability. This prediction equation for use with a SPHYG18cm provided a valid way to estimate lower-limb AOP without expensive equipment.
Landslides are among the most perilous hazards that usually happen in hilly terrains. The loss that ensues during a landslide, especially in highly-populated regions, calls for a vulnerability study. Thus, the purpose of this research is to detect landslide-vulnerable villages in a small part of the Western Ghats, an orographic mountain chain in South India that is proverbially prone to landslides. The study also evaluates the prediction capabilities of analytical hierarchy process (AHP) and ensemble fuzzy-AHP (F-AHP) models. 22 vulnerability indicators (11 physical-environmental and 11 socio-economic) served as the basis for this modeling. These data, derived both from field studies and remotely sensed satellite data, were collated in a geographic information system (GIS) environment, and landslide vulnerability maps were generated. Landslide vulnerability modeling using AHP and F-AHP models found 12.07% and 4.53% of the region, respectively, as very high-vulnerable. The developed landslide vulnerability maps are validated using the receiver operating characteristic (ROC) curve, sensitivity, specificity, Kappa index, mean squared error (MSE), and root mean squared error (RMSE) techniques. Based on the area under the ROC curve (AUC) scores, the landslide vulnerability maps developed utilizing these models were found to be outstanding. With an AUC score of 96.55% (0.96), the ensemble (F-AHP) was found to be more competent than the AHP model, which had an AUC value of 95.14% (0.95). The sensitivity, specificity, Kappa index, MSE, and RMSE values for the F-AHP model are 95.14%, 93.61%, 94.35%, 0.091, and 0.211, respectively, and for the AHP model, they are 92.93%, 94.04%, 92.45%, 0.099, and 0.228. Hence, in this study area, it can be affirmed that F-AHP is the better model for distinguishing vulnerable zones. As per the F-AHP model, Vellarada village is very highly vulnerable, and villages, namely Keezharoor and the western part of Peringamala, Vithura, and Mannoorkara, are highly vulnerable to landslides.
Shoreline cities are influenced by both urban‐scale processes and land‐water interactions, with consequences on heat exposure and its disparities. Heat exposure studies over these cities have focused on air and skin temperature, even though moisture advection from water bodies can also modulate heat stress. Here, using an ensemble of model simulations covering Chicago, we find that Lake Michigan strongly reduces heat exposure (2.75°C reduction in maximum average air temperature in Chicago) and heat stress (maximum average wet bulb globe temperature reduced by 0.86°C) during the day, while urbanization enhances them at night (2.75 and 1.57°C increases in minimum average air and wet bulb globe temperature, respectively). We also demonstrate that urban and lake impacts on temperature (particularly skin temperature), including their extremes, and lake‐to‐land gradients, are stronger than the corresponding impacts on heat stress, partly due to humidity‐related feedback. Likewise, environmental disparities across community areas in Chicago seen for skin temperature are much higher (1.29°C increase for maximum average values per $10,000 higher median income per capita) than disparities in air temperature (0.50°C increase) and wet bulb globe temperature (0.23°C increase). The results call for consistent use of physiologically relevant heat exposure metrics to accurately capture the public health implications of urbanization.
We employed several algorithms with high efficacy to analyze the public transcriptomic data, aiming to identify key transcription factors (TFs) that regulate regeneration in Arabidopsis thaliana. Initially, we utilized CollaborativeNet, also known as TF-Cluster, to construct a collaborative network of all TFs, which was subsequently decomposed into many subnetworks using the Triple-Link and Compound Spring Embedder (CoSE) algorithms. Functional analysis of these subnetworks led to the identification of nine subnetworks closely associated with regeneration. We further applied principal component analysis and gene ontology (GO) enrichment analysis to reduce the subnetworks from nine to three, namely subnetworks 1, 12, and 17. Searching for TF-binding sites in the promoters of the co-expressed and co-regulated (CCGs) genes of all TFs in these three subnetworks and Triple-Gene Mutual Interaction analysis of TFs in these three subnetworks with the CCGs involved in regeneration enabled us to rank the TFs in each subnetwork. Finally, six potential candidate TFs-WOX9A, LEC2, PGA37, WIP5, PEI1, and AIL1 from subnetwork 1-were identified, and their roles in somatic embryogenesis (GO:0010262) and regeneration (GO:0031099) were discussed, so were the TFs in Subnetwork 12 and 17 associated with regeneration. The TFs identified were also assessed using the CIS-BP database and Expression Atlas. Our analyses suggest some novel TFs that may have regulatory roles in regeneration and embryogenesis and provide valuable data and insights into the regulatory mechanisms related to regeneration. The tools and the procedures used here are instrumental for analyzing high-throughput transcriptomic data and advancing our understanding of the regulation of various biological processes of interest.
Permafrost warming and degradation is well documented across the Arctic. However, observation‐ and model‐based studies typically consider thaw to occur at 0°C, neglecting the widespread occurrence of saline permafrost in coastal plain regions. In this study, we document rapid saline permafrost thaw below a shallow arctic lake. Over the 15‐year period, the lakebed subsided by 0.6 m as ice‐rich, saline permafrost thawed. Repeat transient electromagnetic measurements show that near‐surface bulk sediment electrical conductivity increased by 198% between 2016 and 2022. Analysis of wintertime Synthetic Aperture Radar satellite imagery indicates a transition from a bedfast to a floating ice lake with brackish water due to saline permafrost thaw. The regime shift likely contributed to the 65% increase in thermokarst lake lateral expansion rates. Our results indicate that thawing saline permafrost may be contributing to an increase in landscape change rates in the Arctic faster than anticipated.
Motivation: Genome-wide association studies (GWAS) is an essential tool for analyzing associations between phenotypes and single nucleotide polymorphisms (SNPs). Most of binary phenotypes in large biobanks are extremely unbalanced, which leads to inflated type I error rates for many widely used association tests for joint analysis of multiple phenotypes. In this article, we first propose a novel method to construct a Multi-Layer Network (MLN) using individuals with at least one case status among all phenotypes. Then, we introduce a computationally efficient community detection method to group phenotypes into disjoint clusters based on the MLN. Finally, we propose a novel approach, MLN with Omnibus (MLN-O), to jointly analyze the association between phenotypes and a SNP. MLN-O uses the score test to test the association of each merged phenotype in a cluster and a SNP, then uses the Omnibus test to obtain an overall test statistic to test the association between all phenotypes and a SNP. Results: We conduct extensive simulation studies to reveal that the proposed approach can control type I error rates and is more powerful than some existing methods. Meanwhile, we apply the proposed method to a real data set in the UK Biobank. Using phenotypes in Chapter XIII (Diseases of the musculoskeletal system and connective tissue) in the UK Biobank, we find that MLN-O identifies more significant SNPs than other methods we compare with. Availability: https://github.com/Hongjing-Xie/Multi-Layer-Network-with-Omnibus-MLN-O. Supplementary information: Supplementary data are available at Bioinformatics online.
Scientist have demonstrated substantial interest in the biosynthesis of metallic nanoparticles, particularly for their applications in the fields of bio-nanotechnology and medicine. Our study specifically explores the biosynthesis of copper-silver bimetallic nanoparticles (Cu-Ag BMNPs) using Argyreia Nervosa (AN) plant leaf green extract as a versatile agent for capping, reducing, and stabilizing. This biosynthesis method is characterized by its simplicity and cost-effectiveness, utilizing silver nitrate (AgNO3) and cupric oxide (CuO) as precursor materials. Our comprehensive characterization of the Cu-Ag BMNPs, employing techniques such as X-Ray Diffraction (XRD), UV-Vis Spectrometry, Scanning Electron Microscopy (SEM), Zeta Sizer, and Fourier Transformed Infrared Spectrometry (FTIR), unveiled important structural and compositional details. The FTIR results confirmed the successful removal of organic and inorganic impurities, findings supported by the XRD data. To evaluate the antimicrobial properties of the Cu-Ag BMNPs, we conducted disk diffusion and Minimum Inhibitory Concentration (MIC) assays against Escherichia coli (E. coli), with results compared to the standard Gentamicin antibiotic. These assays showcased the improved antimicrobial activity of Cu-Ag bimetallic nanoparticles, highlighting their synergistic effect, characterized by high MIC values and a broad zone of inhibition in the disc diffusion tests against E. coli. These results emphasize the significant antibacterial potential of the synthesized BMNPs, with a medicinal plant Argyreia Nervosa leaf extract playing a pivotal role in enhancing this activity.
Travel time prediction (TTP) is an important module task to support various applications for Internet of Vehicles (IoVs). Although TTP has been widely investigated in the existing literature, most of them assume that the traffic data for estimating the travel time are comprehensive and public for free. However, accurate TTP needs real-time vehicular data so that the prediction can be adaptive to traffic changes. Moreover, since real-time data contain vehicles’ privacy, TTP requires protection during the data processing. In this paper, we propose a novel Privacy-Preserving TTP mechanism for IoVs, \(\mathbb{P}\mathbb{T}\)Prediction, based on crowdsensing and federated learning. In crowdsensing, a data curator continually collects traffic data from vehicles for TTP. To protect the vehicles’ privacy, we make use of the federated learning so that vehicles can help the data curator train the prediction model without revealing their information. We also design a spatial prefix encoding method to protect vehicles’ location information, along with a ciphertext-policy attribute-based encryption (CP-ABE) mechanism to protect the prediction model of the curator. We evaluate \(\mathbb{P}\mathbb{T}\)Prediction in terms of MAE, MSE, RMSE on two real-world traffic datasets. The experimental results illustrate that the proposed \(\mathbb{P}\mathbb{T}\)Prediction shows higher prediction accuracy and stronger privacy protection comparing to the existing methods.
When it is in the template RNA, the naturally occurring m ¹ A epitranscriptomic RNA modification was recently reported to be able to stop the RNA polymerization reaction catalyzed by the RNA dependent RNA polymerase (RdRp) of SARS-CoV-2. In this report, we report that m ¹ A via its triphosphate form (m ¹ ATP) can be incorporated into RNA by the same RdRp. These two findings point a new direction for antiviral drug development based on m ¹ A for combatting COVID-19. More broadly, it is possible that the large pool of epigenetic RNA as well as DNA modifications could serve as a treasury for drug discovery aimed at combating various infectious and other diseases.
The design of efficient photocatalysts for dye degradation is a challenging task for the scientific community. Semiconductor-based photocatalysts such as g-C3N4 and oxides, utilizing solar energy, have been proven to be effective and promising approaches to resolve this issue to some extent. Constructing Z-scheme heterostructures by coupling g-C3N4 with suitable oxide semiconductors has shown substantial enhancement of the photocatalytic performance. In this article, perovskite-type CeMnO3 (5, 15, 25%) nanoparticle-decorated g-C3N4 nanosheets are fabricated as heterostructures, using a hydrothermal synthesis process, for efficient photocatalysis of organic dyes. The formations of heterostructures are confirmed through structural, microstructural, and elemental state analysis. Brunauer–Emmett–Teller (BET) and Barrett–Joyner–Halenda (BJH) characterization techniques exhibited enhanced surface area and pore sizes, respectively. Ultraviolet–visible (UV–vis) diffuse reflectance spectroscopy (DRS), Mott–Schottky, and linear sweep voltammetry (LSV) analyses along with density functional theory (DFT) calculations predicted a p–n junction heterostructure. Electron paramagnetic resonance (EPR) studies revealed a broad spectrum with sextet hyperfine lines corresponding to Mn4+ and Mn2+ ions and enhanced intensity as compared to the parent ones, signifying the creation of oxygen vacancies in the heterostructure. The CeMnO3 (25 wt %)/g-C3N4 heterostructure showed highly efficient photocatalytic degradation of methylene blue under direct sunlight irradiation, with up to 99% degradation achieved in 120 min and excellent recyclability. The robustness of this photocatalyst was tested by adopting a similar process for methylene orange dye degradation, exhibiting 94% yield in 120 min. A tentative degradation mechanism is proposed based on the enhanced photodegradation efficiency and results obtained from electrochemical impedance (EIS), photoluminescence (PL), LSV, and first principal studies, which provides more insights into the photogenerated charge separation, enhanced photocurrent, and interfacial transfer efficiency through the Z-scheme charge transfer process. This study offers opportunities for designing high-performance Z-scheme hybrid photocatalysts for environmental remediation.
Great Lakes coastlines are mosaics of wetland, stream, and lake habitats, characterized by a high degree of spatial heterogeneity that may facilitate the co-occurrence of seemingly incompatible biogeochemical processes due to variation in environmental factors that favor each process. We measured nutrient limitation and rates of N2 fixation and denitrification along transects in 5 wetland–stream–lake ecotones with different nutrient loading in Lakes Superior and Huron. We hypothesized that rates of both processes would be related to nutrient limitation status, habitat type, and environmental characteristics including temperature, nutrient concentrations, and organic matter quality. We found that median denitrification rates (914 μg N m⁻² h⁻¹) were 166 × higher than N2 fixation rates (5.5 μg N m⁻² h⁻¹), but the processes co-occurred in 48% of 83 points measured across all 5 transects and habitat types. N2 fixation occurred on sediment and macrophyte substrate, while denitrification occurred mostly in sediment. Nutrient-diffusing substrate experiments indicated that biofilm chlorophyll-a was limited by N and/or P at 55% and biofilm AFDM was limited at 26% of sample points. N2 fixation and denitrification rates did not differ significantly with differing nutrient limitation. Predictive models for N2 fixation and denitrification rates both included variables related to the composition of dissolved organic matter, while the model for N2 fixation also included P concentrations. These results demonstrate the potential for heterogeneity in habitat characteristics, nutrient availability, and organic matter composition to lead to biogeochemical complexity at the local scale, despite overall N removal at broader scales.
Composite structures in transportation industries have gained significant attention due to their unique characteristics, including high energy absorption. Non-destructive testing methods coupled with machine learning techniques offer valuable insights into failure mechanisms by analyzing basic parameters. In this study, damage monitoring technologies for composite tubes experiencing progressive damage were investigated. The challenges associated with quantitative failure monitoring were addressed, and the Genetic K-means algorithm, hierarchical clustering, and artificial neural network (ANN) methods were employed along with other three alternative methods. The impact characteristics and damage mechanisms of composite tubes under axial compressive load were assessed using Acoustic Emission (AE) monitoring and machine learning.Various failure modes such as matrix cracking, delamination, debonding, and fiber breakage were induced by layer bending. An increase in fibers/matrix separation and fiber breakage was observed with altered failure modes, while matrix cracking decreased Signal classification was achieved using hierarchical and K-means genetic clustering methods, providing insights into failure mode frequency ranges and corresponding amplitude ranges. The ANN model, trained with labeled data, demonstrated high accuracy in classifying data and identifying specific failure mechanisms. Comparative analysis revealed that the Random Forest model consistently outperformed the ANN and Support Vector Machine (SVM) models, exhibiting superior predictive accuracy and classification using ACC, MCC and F1-Score metrics. Moreover, our evaluation emphasized the Random Forest model's higher true positive rates and lower false positive rates. Overall, this study contributes to the understanding of model selection, performance assessment in machine learning, and failure detection in composite structures.
O-linked β-N-acetylglucosamine (O-GlcNAc) is a distinct monosaccharide modification of serine (S) or threonine (T) residues of nucleocytoplasmic and mitochondrial proteins. O-GlcNAc modification (i.e., O-GlcNAcylation) is involved in the regulation of diverse cellular processes, including transcription, epigenetic modifications, and cell signaling. Despite the great progress in experimentally mapping O-GlcNAc sites, there is an unmet need to develop robust prediction tools that can effectively locate the presence of O-GlcNAc sites in protein sequences of interest. In this work, we performed a comprehensive evaluation of a framework for prediction of protein O-GlcNAc sites using embeddings from pre-trained protein language models. In particular, we compared the performance of three protein sequence-based large protein language models (pLMs), Ankh, ESM-2, and ProtT5, for prediction of O-GlcNAc sites and also evaluated various ensemble strategies to integrate embeddings from these protein language models. Upon investigation, the decision-level fusion approach that integrates the decisions of the three embedding models, which we call LM-OGlcNAc-Site, outperformed the models trained on these individual language models as well as other fusion approaches and other existing predictors in almost all of the parameters evaluated. The precise prediction of O-GlcNAc sites will facilitate the probing of O-GlcNAc site-specific functions of proteins in physiology and diseases. Moreover, these findings also indicate the effectiveness of combined uses of multiple protein language models in post-translational modification prediction and open exciting avenues for further research and exploration in other protein downstream tasks. LM-OGlcNAc-Site’s web server and source code are publicly available to the community.
Asphalt pavement is the most widely used type of pavement in the world and is mainly utilized in the construction of infrastructures such as highways, urban roads, parking lots, and airstrips. The pavement maintenance technology and materials are gradually developing towards systematization and diversification with the extensive use of asphalt pavement. Choosing more economical technologies, and fast and sustainable materials is the future of asphalt pavement maintenance work. This paper provides an overview of asphalt pavement repair technology and asphalt pavement repair materials. We categorize and summarize the pavement repair technologies, which include cost-effective technologies such as crack sealing, overlay, seal coat, and hot-in-place recycling. Further, we summarized the repair materials applied in the repair technologies and compared the performance, cost-effectiveness, and sustainability of each type of material. The study shows that asphalt and cement are the most commonly used repair materials. The more potential, economical, and sustainable materials for asphalt pavement repair include epoxy resin, polyurethane, and hydrogel. The future of asphalt pavement repair requires advancing towards rapid, sustainable, environmentally friendly, and economical. The survey indicates that in addition to improving the performance of existing repair materials (e.g., modified asphalt, binder processes, material composition or ratios), attention can be given to new materials such as polyurethanes and hydrogels that have the potential for rapid repair and at low cost. More research can be done to perfect the application of new materials in road engineering.
In this paper, we study the transmission eigenvalue problem for an anisotropic material with a conductive boundary. We prove that the transmission eigenvalues for this problem exist and are at most a discrete set. We also study the dependence of the transmission eigenvalues on the physical parameters and prove that the first transmission eigenvalue is monotonic. We then consider the limiting behavior of the transmission eigenvalues as the conductive boundary parameter η vanishes or goes to infinity in magnitude. Finally, we provide some numerical examples on three different domains to demonstrate our theoretical results.
Efficient detection of selected persistent organic pollutants (POPs) is extremely important for the safety of humans and for the moderation of agriculture. This calls for the design of versatile nanosensors capable of sensing toxic POPs with high sensitivity and selectivity. Inspired by this, the sensing characteristics of carbon nitride (C 3 N 5 ) monolayers toward selected POPs are reported, such as Dichlorodiphenyltrichloroethane (DDT), Methoxychlor (DMDT), Fenthion (FT), Fenitrothion (FNT), and Rennol (RL), employing density functional theory calculations. Analysis of results predicts adsorption energies of −0.93, −1.55, −1.44, −0.98, and −1.15 eV for DDT, DMDT, FT, FNT, and RM, respectively, on C 3 N 5 monolayers. Significant charge transfers among organic pollutants and C 3 N 5 lead to distinct electronic properties of the conjugated complexes, revealed by the density of states, electrostatic potential, and work function calculations. To detect the selected pollutants in high humidity, the effects due to aqueous medium are considered. Additionally, a statistical thermodynamic analysis utilizing the Langmuir adsorption model is utilized to explore the influence of temperature and pressure.
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3,738 members
Dana Johnson
  • College of Business
Nancy HF French
  • Michigan Tech Research Institute
Bowen Li
  • Department of Materials Science & Engineering
William H Cooke
  • Department of Kinesiology and Integrative Physiology
Sean J Kirkpatrick
  • Department of Biomedical Engineering
1400 Townsend Dr, 49931, Houghton, Michigan, United States