Michigan Technological University

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    24283 research outputs found

    Distributed generation hosting capacity analysis: An approach using interval-affine arithmetic and power flow sensitivities

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    The climate change concerns, the decarbonization policies, and the technological advances allied to cost reduction form a set of favorable circumstances for large deployment of renewable-based distributed generation into the power distribution systems. However, the connection of distributed generation significantly affects the power system and its technical impacts must be evaluated. Therefore, the hosting capacity analysis has gained attention as it outputs the maximum amount of distributed generation that can be safely connected. The deterministic and stochastic methods are commonly found in the literature. However, there is a lack of hosting capacity studies based on interval analysis. Therefore, this paper proposes an interval/affine arithmetic-based hosting capacity framework, considering two performance indexes (overvoltage and ampacity), and uses a different approach for modeling generation and load curves as a finite set of correlated affine combinations. The proposed framework enables faster analysis and outputs results that are comparable to the ones from time series simulation. The proposed method also allows less conservative results, depending on the desired level of overvoltage or overload risk

    An extra-large Stokes shift near-infrared fluorescent probe for specific detection and imaging of cysteine

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    Cysteine (Cys) plays a crucial role in numerous physiological and pathological processes. Therefore, it is imperative to design a highly selective and sensitive near-infrared (NIR) fluorescent probe to monitor Cys. In this study, we have developed a novel NIR fluorescent probe XA based on Xanthene hybrid tetrahydro-acridine salt dye for specifically tracking of Cys, where a chlorine-substituted tetrahydro-acridine acts as a high Cys-reactive site and water-soluble group. Probe XA exhibits a remarkable turn-on NIR emission (830 nm) with an extra-large Stokes shift (305 nm) for monitoring Cys. It also has a high selectivity, rapid response time (6 min) and high sensitivity (LOD as 0.5 μM). We fully characterized and discussed the sensing mechanism of XA toward Cys using HPLC and MS spectrums, as well as quantum theory calculations. Furthermore, the excellent properties of NIR fluorescent detection allow this novel probe to successfully monitor fluctuations of exogenous and endogenous Cys concentration levels in living cells and in vivo

    Understanding the Microscopic Mechanism of Clogging of both Fibrous and Mesh Filters in Flooded-bed Wet Scrubbers

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    Flooded-bed scrubbers are commonly employed in continuous miners to capture and remove dust particles at the cutting face in underground coal mines. To date, the mechanism of filter clogging as well as the evolution of pressure drop has not been well understood. In this work, clogging of filters in a relevant flooded-bed scrubber environment have been investigated in a laboratory setting by spraying a slurry onto a filter assembly while pulling the air through. Two types of filters have been investigated, including a) a multi-layer mesh filter and 2) a fibrous woven filter. Results showed that the differential pressure (or pressure drop) increased with an accumulation of clogged particles within the filter. The timed evolution of the pressure drop varied with both pore size and pore structure of the filter medium. When dust particles were much smaller than the pore size of the filter, dust particles predominantly accumulated inside the filter pack. When dust particles had comparable or larger sizes compared with the pore (or mesh) size of the filter medium, the filter was dominantly clogged at its front layers. Experimental results were compared with a modified model to account for the development of the pressure drop across the filter. The clogging of scrubber filters may be presented in two mechanisms, namely a) internal clogging and b) caking. The present result demonstrates the fundamental mechanism of clogging of dust particles within fibrous and mesh filters during dust capture and removal by flooded-bed wet scrubbers as well as other engineering applications involving in multi-phase interaction within a porous medium

    Microfluidic reactor designed for time-lapsed imaging of pretreatment and enzymatic hydrolysis of lignocellulosic biomass

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    The effect of tissue-specific biochemical heterogeneities of lignocellulosic biomass on biomass deconstruction is best understood through confocal laser scanning microscopy (CLSM) combined with immunohistochemistry. However, this process can be challenging, given the fragility of plant materials, and is generally not able to observe changes in the same section of biomass during both pretreatment and enzymatic hydrolysis. To overcome this challenge, a custom polydimethylsiloxane (PDMS) microfluidic imaging reactor was constructed using standard photolithographic techniques. As proof of concept, CLSM was performed on 60 μm-thick corn stem sections during pretreatment and enzymatic hydrolysis using the imaging reactor. Based on the fluorescence images, the less lignified parenchyma cell walls were more susceptible to pretreatment than the lignin-rich vascular bundles. During enzymatic hydrolysis, the highly lignified protoxylem cell wall was the most resistant, remaining unhydrolyzed even after 48 h. Therefore, imaging thin whole biomass sections was useful to obtain tissue-specific changes during biomass deconstruction

    Gamma-ray irradiation to achieve high tensile performance of unidirectional CNT yarn laminates

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    Continuous carbon nanotube (CNT) yarn fabricated from a floating catalyst chemical vapor deposition (FCCVD) method is treated under gamma-ray irradiation to enhance the mechanical properties of the CNT yarn and its unidirectional composite laminates. Gammy-ray doses varying from 50 kGy to 1200 kGy are used to irradiate CNT yarns and their microstructures, tensile properties and surface characterizations are studied. The graphitic structure change is not clear from the transmission electron microscopy, however, the specific tensile strength and modulus of yarn vary slightly within 10 % as the dose increased. This modulus trend coincides with mesoscopic distinct element modeling (mDEM) simulation results. Surface characterization shows additional oxygen functional groups and smaller contact angles after irradiation. Interestingly, the specific tensile properties of composite laminates also increase relative to the yarns, and the unidirectional laminate from CNT yarn treated with the optimal dose of 700 kGy achieves specific strength and modulus as high as 1.89 GPa/gcm−3 and 258 GPa/gcm−3, respectively, which are 30.9 % and 37 % increases compared to the control laminate. The results indicate that radiation-induced crosslinking among the CNTs and the formation of surface-active sites leads to enhanced load transfer in the yarns and promote CNT/resin interfacial bonding

    SeGDroid: An Android malware detection method based on sensitive function call graph learning[Formula presented]

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    Malware is still a challenging security problem in the Android ecosystem, as malware is often obfuscated to evade detection. In such case, semantic behavior feature extraction is crucial for training a robust malware detection model. In this paper, we propose a novel Android malware detection method (named SeGDroid) that focuses on learning the semantic knowledge from sensitive function call graphs (FCGs). Specifically, we devise a graph pruning method to build a sensitive FCG on the base of an original FCG. The method preserves the sensitive API (security-related API) call context and removes the irrelevant nodes of FCGs. We propose a node representation method based on word2vec and social-network-based centrality to extract attributes for graph nodes. Our representation aims at extracting the semantic knowledge of the function calls and the structure of graphs. Using this representation, we induce graph embeddings of the sensitive FCGs associated with node attributes using a graph convolutional neural network algorithm. To provide a model explanation, we further propose a method that calculates node importance. This creates a mechanism for understanding malicious behavior. The experimental results show that SeGDroid achieves an F-score of 98% in the case of malware detection on the CICMal2020 dataset and an F-score of 96% in the case of malware family classification on the MalRadar dataset. In addition, the provided model explanation is able to trace the malicious behavior of the Android malware

    Ectopic expression of PmGRF7 isolated from Japanese apricot in tomato leads to seed sterility

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    The role of GRF (Growth-regulating Factor) in regulating plant growth and development has been studied, but its potential role in regulating seed fertility remains unclear. PmGRF7 overexpression in tomatoes altered leaf morphology and pollen activity. Most importantly, genetically modified tomato plants had no offspring. PmGRF7 overexpression resulted in abnormal leaf morphology, and transgenic plants that produced seedless fruit or seeds only showed signs of abortion. Pollination of genetically modified tomatoes with wild-type tomato pollen still produced sterile seeds. Further research using bioinformatics tools and physiological index measurement showed that PmGRF7 induced a hormone pathway leading to seed sterility, and at the same time, it caused differential genes to be induced in the plant hormone signal transduction pathway, ribosome pathway, sphingolipid metabolism and other related pathways, leading to morphogenesis in plant leaves. Collectively, our research findings reveal a potential new mechanism by which PmGRF7 regulates seed fertility and leaf growth, and these findings are of potential application in molecular plant breeding

    Validation of LES-C turbulence models

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    A new family of turbulence models, Large Eddy Simulation with Correction (LES-C) has been proposed in Labovsky (2020), that reduces the modeling error by treating an LES model as a defect solution and then correcting it on the same spatial mesh. Herein, we investigate numerically several LES-C models, that stem from popular LES approaches: Approximate Deconvolution Model (ADM), Leray-α, NS-α, and NS-ω. The resulting LES-C models ADC, Leray-α-C, NS-α-C and NS-ω-C are tested on the two-dimensional problems (flow past a circular object, flow past a step) and on the three-dimensional benchmark problem of turbulent channel flow. In all the numerical tests all LES-C models are shown to outperform their LES counterparts on coarse spatial meshes

    Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research

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    Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; \u3c37 \u3eweeks) or (2) early preterm birth (ePTB; \u3c32 \u3eweeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth

    High-efficiency degradation of Fe-CNs in SPL through microwave-activated persulfate

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    Cyanide in spent potlining (SPL) is primarily present as sodium cyanide (NaCN) and iron (II, III) cyanide complexes (Fe-CNs). NaCN is easy to degrade, while Fe-CNs is extremely difficult to degrade. In this work, a novel microwave-activated persulfate technology (MW-PS) was firstly proposed to degrade Fe-CNs promptly and efficiently. The removal rate of 99.88 % was achieved within 180 s under optimal conditions of pH = 10.15, [oxidant]:[Fe-CNs] molar ratio of 5:1, and microwave power of 300 W. The remaining total cyanide content was less than 0.04 mg·L−1. The degradation mechanism of ferrocyanide (II) and ferricyanide (III) in the MW-PS degradation system was systematically analyzed through electron paramagnetic resonance (EPR) and element identification techniques. The ROS (SO4[rad]−, HO[rad], O2[rad]−, and 1O2) generated under microwave activation and first attacked the C-Fe complex bond in Fe-CNs, causing the bond to dissociate and release the free CN−, and then CN− was thoroughly oxidized. This work develops a simple and high-efficiency decyanation technology for complex cyanide contained industrial waste disposal

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