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1. Electrostatic Spray Disinfection Using Nano-Engineered Solution on Frequently Touched Surfaces in Indoor and Outdoor Environments

   Tanya Purwar 1, * (May 2022, International journal of environmental research and public health)

   Paul B. Tchounwou (Ed.)

   Abstract: The COVID-19 pandemic has resulted in high demand for disinfection technologies. However, the corresponding spray technologies are still not completely optimized for disinfection purposes. There are important problems, like the irregular coverage and dripping of disinfectant solutions on hard and vertical surfaces. In this study, we highlight two major points. Firstly, we discuss the effectiveness of the electrostatic spray deposition (ESD) of nanoparticle-based disinfectant solutions for systematic and long-lasting disinfection. Secondly, we show that, based on the type of material of the substrate, the effectiveness of ESD varies. Accordingly, 12 frequently touched surface materials were sprayed using a range of electrostatic spray system parameters, including ion generator voltage, nozzle spray size and distance of spray. It was observed that for most cases, the surfaces become completely covered with the nanoparticles within 10 s. Acrylic, Teflon, PVC, and polypropylene surfaces show a distinct effect of ESD and non-ESD sprays. The nanoparticles form a uniform layer with better surface coverage in case of electrostatic deposition. Quantitative variations and correlations show that 1.5 feet of working distance, an 80 μm spray nozzle diameter and an ion generator voltage of 3–7 kV ensures a DEF (differential electric field) that corresponds to an optimized charge-to-mass ratio, ensuring efficient coverage of nanoparticles. 

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2. A Sensor Probe with Active and Passive Humidity Management for In Situ Soil CO2 Monitoring

   https://doi.org/10.3390/s24186034  

   Anderson, Jacob F; Huber, David P; Walsh, Owen A (September 2024, Sensors)

   Soil CO2 concentration and flux measurements are important in diverse fields, including geoscience, climate science, soil ecology, and agriculture. However, practitioners in these fields face difficulties with existing soil CO2 gas probes, which have had problems with high costs and frequent failures when deployed. Confronted with a recent research project’s need for long-term in-soil CO2 monitoring at a large number of sites in harsh environmental conditions, we developed our own CO2 logging system to reduce expense and avoid the expected failures of commercial instruments. Our newly developed soil probes overcome the central challenge of soil gas probes—surviving continuous exposure to soil moisture while remaining open to soil gases—via three approaches: a 3D printed housing (economical for small-scale production) following design principles that correct the usual water permeability flaw of 3D printed materials; passive moisture protection via a hydrophobic, CO2-permeable PTFE membrane; and active moisture protection via a low-power micro-dehumidifier. Our CO2 instrumentation performed well and yielded a high-quality dataset that includes signals related to a prescribed fire as well as seasonal and diel cycles. We expect our technology to support underground CO2 monitoring in fields where it is already practiced and stimulate its expansion into diverse new fields. 

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3. A Comparison between Analog Ensemble and Convolutional Neural Network Empirical-Statistical Downscaling Techniques for Reconstructing High-Resolution Near-Surface Wind

   https://doi.org/10.3390/en15051718  

   Rozoff, Christopher M.; Alessandrini, Stefano (March 2022, Energies)

   Empirical-statistical downscaling (ESD) can be a computationally advantageous alternative to dynamical downscaling in representing a high-resolution regional climate. Two distinct strategies of ESD are employed here to reconstruct near-surface winds in a region of rugged terrain. ESD is used to reconstruct the innermost grid of a multiply nested mesoscale model framework for regional climate downscaling. An analog ensemble (AnEn) and a convolutional neural network (CNN) are compared in their ability to represent near-surface winds in the innermost grid in lieu of dynamical downscaling. Downscaling for a 30 year climatology of 10 m April winds is performed for southern MO, USA. Five years of training suffices for producing low mean absolute error and bias for both ESD techniques. However, root-mean-squared error is not significantly reduced by either scheme. In the case of the AnEn, this is due to a minority of cases not producing a satisfactory representation of high-resolution wind, accentuating the root-mean-squared error in spite of a small mean absolute error. Homogeneous comparison shows that the AnEn produces smaller errors than the CNN. Though further tuning may improve results, the ESD techniques considered here show that they can produce a reliable, computationally inexpensive method for reconstructing high-resolution 10 m winds over complex terrain. 

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4. ESD Protection Designs: Topical Overview and Perspective

   Cheng Li, Zijin Pan (July 2022, Nanomaterials)

   Abstract: As the ever increasing demands for performance of integrated circuit (IC) chip continue to increase, while technology scaling driven by Moore’s Law is becoming extremely challenging if not impractical or impossible, heterogeneous integration (HI) emerges as an attractive pathway to further enhance performance of Si-based CMOS chips. The underlying basis for using HI tech-nologies and structures is that IC performance goes well beyond classic logic functions; rather, functionalities and complexity of smart chips span across the full information chain, including signal sensing, conditioning, processing, storage, computing, communication, control and actua-tion, which are required to facilitate comprehensive human-world interactions. Therefore, HI technologies can bring in more function diversifications to make system chips smarter within ac-ceptable design constraints including costs. Over the past two decades or so, a large number of HI technologies have been explored to increase heterogeneities in materials, technologies, devices, circuits and system architectures, making it practically impossible to provide one single compre-hensive review of everything in the field in one paper. This article chooses to offer a topical over-view of selected HI structures that have been validated in CMOS platform, including a stacked-via vertical magnetic-cored inductor structure in CMOS, a metal wall structure in the backend of line (BEOL) of CMOS to suppress global flying noises, an above-IC graphene NEMS switch and a nano crossbar array electrostatic discharge (ESD) protection structure, and graphene ESD interconnects 

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5. Bayesian Estimation of Attribute Hierarchy for Cognitive Diagnosis Models

   https://doi.org/10.3102/10769986231174918  

   Chen, Yinghan; Wang, Shiyu (June 2023, Journal of Educational and Behavioral Statistics)

   Attribute hierarchy, the underlying prerequisite relationship among attributes, plays an important role in applying cognitive diagnosis models (CDM) for designing efficient cognitive diagnostic assessments. However, there are limited statistical tools to directly estimate attribute hierarchy from response data. In this study, we proposed a Bayesian formulation for attribute hierarchy within CDM framework and developed an efficient Metropolis within Gibbs algorithm to estimate the underlying hierarchy along with the specified CDM parameters. Our proposed estimation method is flexible and can be adapted to a general class of CDMs. We demonstrated our proposed method via a simulation study, and the results from which show that the proposed method can fully recover or estimate at least a subgraph of the underlying structure across various conditions under a specified CDM model. The real data application indicates the potential of learning attribute structure from data using our algorithm and validating the existing attribute hierarchy specified by content experts. 

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