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Adaptive Radio Frequency Target Localization
No full textMobile radio frequency (RF) target localization is a widely studied field with a wide variety of applications including health monitoring of electronic devices and search and rescue. This recently has been done using a greedy approach, where a sensor is moved closer to the target after each measurement. However, this does not allow for other constraints, such as maintaining a fixed distance from the target or optimization of energy consumption. Studies in the field have used techniques such as machine learning to localize static targets with received RF signals, and dynamic targets with received RF signals combined with line-of-sight observations. A recent study simulated localization of static and dynamic targets through RF signal characterization without the need for line-of-sight observation while also maintaining the previously mentioned constraints. This was done by modeling the problem as a Partially Observable Markov Decision Process (POMDP) and was solved through the use of particle filtering and reinforcement learning. The purpose of this work is to build upon this prior study by training a deep neural network in a simulated environment and applying inference in the real world. This led to various changes to the model that better matched real-world scenarios. By defining the metric of success as the distance between the actual location of the device and the estimated location of the device, it was shown that the model was able to accurately locate static devices within the measured standard deviation of the signal strength. Future work includes the use of autonomous units such as drones, as well as extending the capabilities of the model to localize real-world dynamic targets
Improved physics-informed neural networks for the reinterpreted discrete fracture model
No full textThis paper is the first attempt to apply improved-physics-informed neural networks (I-PINNs) to simulate fluid flow in fractured porous media based on the reinterpreted discrete fracture model (RDFM). The RDFM, first introduced by Xu and Yang, is a hybrid-dimensional model where Dirac-delta functions are used to characterize fractures and superposed with the permeability tensor. In this paper, we apply the physical information neural networks (PINNs) to RDFM. Different from the traditional PINNs where the PDE residual was used as the loss function, we adopt the finite element discretization of RDFM to build the loss function, avoiding the large gradient problem and difficulties in automatic differentiation. This new method is named as the improved PINNs (I-PINNs). Moreover, we combine the RDFM with incompressible miscible displacement in porous media. The bound-preserving technique of the I-PINNs is proposed and applied to the coupled system mentioned above, keeping the numerical concentration to be between 0 and 1. It is worth noting that one of the advantages of I-PINNs compared to PINNs is that it can better capture the pressure gradient at the fractures. Compared with traditional finite element methods for flow equations, I-PINNs do not request the inversion of the stiffness matrix. In addition, different from the traditional bound-preserving technique for contaminant transportation, I-PINNs preserve the physical bounds without taking a limited time step. Several numerical experiments are given to verify the feasibility and accuracy of the I-PINNs
Investigating the structure-property correlations of pyrolyzed phenolic resin as a function of degree of carbonization
Get PDFCarbon-carbon (C/C) composites are attractive materials for high-speed flights and terrestrial atmospheric reentry applications due to their insulating thermal properties, thermal resistance, and high strength-to-weight ratio. It is important to understand the evolving structure-property correlations in these materials during pyrolysis, but the extreme laboratory conditions required to produce C/C composites make it difficult to quantify the properties in situ. This work presents an atomistic modeling methodology to pyrolyze a crosslinked phenolic resin network and track the evolving thermomechanical properties of the skeletal matrix during simulated pyrolysis. First, the crosslinked resin is pyrolyzed and the resulting char yield and mass density are verified to match experimental values, establishing the model\u27s powerful predictive capabilities. Young\u27s modulus, yield stress, Poisson\u27s ratio, and thermal conductivity are calculated for the polymerized structure, intermediate pyrolyzed structures, and fully pyrolyzed structure to reveal structure-property correlations, and the evolution of properties are linked to observed structural features. It is determined that reduction in fractional free volume and densification of the resin during pyrolysis contribute significantly to the increase in thermomechanical properties of the skeletal phenolic matrix. A complex interplay of the formation of six-membered carbon rings at the expense of five and seven-membered carbon rings is revealed to affect thermal conductivity. Increased anisotropy was observed in the latter stages of pyrolysis due to the development of aligned aromatic structures. Experimentally validated predictive atomistic models are a key first step to multiscale process modeling of C/C composites to optimize next-generation materials
Numerical simulation of hollow-cone hydrogen underexpanded jet injection using a phenomenological barrel shock model
No full textIn the present study, a gas injection model for simulating transient direct injection of hollow-cone hydrogen jet into a combustion chamber using a practical computational grid was developed. The model was implemented into MTU-MRNT code, an in-house multi-dimensional CFD code. The new model employs a phenomenological barrel shock model coupled with a gas sphere injection model to describe the physical phenomena of high speed hydrogen injection. The underexpanded jet issuing from the nozzle was modeled using the conditions at the exit of the barrel shock that is obtained using the characteristic shock structure of underexpanded jet. The model considers time-varying pressure downstream of the injection nozzle by using the quasi-steady state flow assumption at each time-step. A non-ideal gas model was employed to accurately describe the physical properties of hydrogen gas experiencing expansion and shock waves in the nozzle and subsequent underexpanded jet flow. The model was applied to simulate a hollow-cone hydrogen jet injection into a constant volume chamber and the results are compared with available experimental literature data. The predicted and experimental jet penetration profiles were in good agreement. The non-ideal gas model captures hydrogen thermodynamic properties accurately and improves the prediction accuracy of hydrogen jet penetration. The present model not only successfully predicts hydrogen jet behavior with a coarse grid, but also substantially contributes to the computational efficiency in numerical simulations of practical hydrogen applications
Construction and optimization of asphalt pavement texture characterization model based on binocular vision and deep learning
No full textTo efficiently characterize the texture of asphalt pavements, a BCTD (Binocular Camera Texture Detection) system is developed based on the principles of binocular stereo vision technology. The system introduces an innovative approach to texture analysis using self-invented TDFA (Tire-pavement Dynamic Friction Analyzer) equipment and long-term anti-skid performance testing. The system facilitates the collection and pre-processing of pavement texture images, achieving an average TOP1 accuracy of 67 % with a measurement precision of 0.1 mm. The results indicate that the model exhibits excellent recognition performance for weak feature images within the same type of pavement texture, effectively characterizing the texture of asphalt pavements. In summary, this study provides a comprehensive and innovative approach to asphalt pavement texture characterization. It advances the field by providing valuable insights into texture analysis, particularly weak features. The BCTD system demonstrates the potential of monitoring the skid resistance of asphalt pavements to improve road safety and maintenance efficiency
Spatial Heterogeneity of Nitrogen Fixation and Denitrification in Streams
No full textStream ecosystems exhibit high degrees of spatial heterogeneity at nested scales from microhabitats to regions. This heterogeneity may facilitate the co-occurrence of biogeochemical processes that are favored under incompatible environmental conditions, like dinitrogen (N2 gas) fixation and denitrification. We hypothesized that environmental variation at the patch scale (1–10’s m) would facilitate the co-occurrence of N2 fixation and denitrification through the formation of hot spots. We measured rates of N2 fixation and denitrification and relative abundances of nifH and nirS (genes that encode for the enzymes nitrogenase and nitrite reductase, respectively) in patches determined by channel geomorphic units and substratum type in seven streams encompassing a gradient of N and P concentrations. We found hot spots, where rates of N2 fixation and denitrification were 1–4 times higher than reach-average rates, in all study streams. Most N2 fixation hot spots were in patches with rock substrata, while denitrification rates and relative abundances of nifH and nirS were higher in patches with fine sediment. Yet, in one of the streams, the same patches hosted rates in the top 25% of all patches for both denitrification and N2 fixation. Across all streams and patches, organic matter and dissolved oxygen concentrations were important predictors of rates of N2 fixation, denitrification, and nifH relative abundance, while P concentration was important to N2 fixation and denitrification. Our results demonstrate that understanding the spatial ecology of microbially driven nutrient cycling is required to characterize nutrient fluxes more completely in stream ecosystems
Experimental characterization and constitutive modeling of bulk epoxy under thermo-oxidative aging
No full textMost research in the area of high-temperature oxidation of polymers focuses on the realm of reaction-limited oxidation, where the effects of oxidation are homogeneous throughout the thickness of the material. While this may be applicable in applications involving coatings, or thin films, most load-bearing structures rely on finite thickness samples (bulk material) or a composite to support the load. These finite-dimension bulk samples typically demonstrate signs of diffusion-limited oxidation, where the chemical reactions with ambient oxygen mostly reside on the surface due to the restricted ability for oxygen to diffuse through the thickness of the material. Hence, a heterogeneous growth of the oxide layer occurs across the thickness of the bulk sample, resulting in localized alteration of the oxidized network mostly near the outer surfaces. As the formation of this oxide layer continues, depending on the aging period, the mechanical response of the bulk sample degrades significantly. In this study, we used various experimental techniques to investigate the correlation between the growth of the oxide layer and the impact of local microstructural changes within this layer on the macroscopic constitutive response of a bulk thermoset (epoxy) as a function of the aging period. We used a controlled environmental chamber to accelerate the high-temperature oxidation of a structural epoxy commonly used in industry. Experimental techniques such as Fourier transform infrared spectroscopy (FTIR), dynamic mechanical analysis (DMA), nano-indentation, and uni-axial tensile testing were used to characterize how heterogeneous microstructural changes due to diffusion-limited oxidation affect the macroscopic performances of these materials. Additionally, a phenomenological constitutive model based on a well-known nonlinear viscoplastic framework was used to describe the uniaxial tensile response of both virgin and oxidized epoxy. This model incorporates the effects of aging-induced degradation by modifying certain material parameters to reflect the microstructural changes in the polymer network. The major findings from the experiments indicated the formation of an oxide layer on the outer surfaces of the epoxy samples, which exhibited a higher indentation modulus. Increased concentrations of carbonyls were detected in this outer layer by the FTIR spectra corroborating oxidative crosslinking events. These local changes in the macromolecular structures resulted in reduced viscoplastic deformation of the aged epoxy at continuum level testing such as uniaxial tensile, and dynamic mechanical response
Geospatial analysis for promoting urban green space equity: Case study of Detroit, Michigan, USA
No full textUrban green spaces play a vital role in promoting human health and well-being, enhancing urban ecosystems, and supporting urban sustainability and resilience. However, inequities in the distribution and accessibility to urban green spaces can disproportionately affect vulnerable and underserved communities. This study examines the distribution and accessibility of urban green spaces in Detroit, Michigan, using high-resolution geospatial data and geospatial analysis methods, including geographically weighted regression (GWR) and network-based analyses. The study aims to correlate urban green space access inequities with social and environmental justice indicators and offer strategies for urban planners to identify and address green space inequities using geospatial analysis. The case study identifies significant urban green space inequities, with 87 % (53 %) of buildings lacking a park or recreational area within a quarter-mile (half-mile) walking distance. GWR analysis further demonstrates that neighborhoods with higher social vulnerability scores tend to have significantly lower green space availability, although park areas appear to be equitably distributed in some parts of the city. These findings highlight critical areas in Detroit that can be prioritized for green space development to address these inequities and create healthier, more resilient urban environments. The methods presented can be applied to other cities to assist urban planners in identifying where resources can be most efficiently allocated to address current green space disparities, particularly in historically underserved areas
Applications of antibody-based sensors in the food sector
No full textAccording to the United Nations, by 2050, the global population is projected to be near nine billion with a subsequent mount in foodstuff requirements that poise an urgency to necessitate food safety, quality, and security accompanied by efficient and effective preservation of the food chain. Several toxic substances such as allergens, toxins, contaminants, or pathogens have been posing a constant threat to human health via food products. Therefore it is of utmost significance to develop devices that have high sensitivity, and are reliable, swift, and cost-effective. Biosensors or immunosensors can prove to be cutting-edge and avant-garde technology in assessing food quality and safety in comparison to the conventional techniques, having limitations such as longer analysis time, expensive, procedural complexity, and require skilled operators. These sensors not only offer real-time estimations/monitoring, extensive automation, and superior throughput, selectivity, and sensitivity but also provide low pricing, lesser sophistication of instruments, faster analysis time, and user-friendliness. Apart from their clinical, environmental, and biotechnological applications, the current chapter embodies various antibody-derived biosensors and their applications specifically in the agri-food industry for assessment of safety and security
Dual Functionality of Cobalt Oxide Nanoparticles: Exploring Their Potential as Antimicrobial and Anticancer Agents
No full textCobalt oxide nanoparticles (Co3O4 NPs) exhibit promising dual functionality as both antimicrobial and anticancer agents, addressing critical challenges posed by microbial resistance and the limitations of conventional cancer therapies. This review provides an overview of the antimicrobial activity of Co3O4 NPs, detailing their mechanisms of action, which include membrane disruption, protein and enzyme dysfunction, and DNA damage. Comparative studies highlight the superior efficacy of Co3O4 NPs against various pathogens relative to traditional antimicrobial agents, emphasizing their potential as a viable alternative in combating resistant strains. In addition to their antimicrobial properties, Co3O4 NPs demonstrate significant anticancer activity, contributing to advancements in cancer therapy through mechanisms such as reactive oxygen species (ROS) generation, mitochondrial dysfunction, DNA damage, and the induction of apoptosis and autophagy. Despite their therapeutic potential, concerns regarding toxicity and biocompatibility remain, necessitating comprehensive in vitro and in vivo toxicity studies to assess their safety profile. Key challenges to the clinical application of Co3O4 NPs include understanding their mechanisms of action, addressing formulation stability, navigating regulatory hurdles, and developing scalable manufacturing processes. Future prospects for Co3O4 NPs lie in synergistic therapeutic applications, personalized medicine tailored to individual patient profiles, integration with emerging technologies, and the exploration of combination nanoparticles that enhance therapeutic efficacy. Furthermore, advanced characterization techniques will be pivotal in elucidating the nanoparticle behavior in biological systems, paving the way for clinical trials that validate their safety and efficacy in treating infections and cancer. This comprehensive analysis underscores the potential of Co3O4 NPs as a multifaceted platform for combating antimicrobial resistance and advancing cancer therapeutics