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1. Drying of a Fully Saturated Porous Medium With Excess Water Layers: A Numerical Study

   https://doi.org/10.1115/1.4056068  

   Asar, Munevver Elif; Yagoobi, Jamal (February 2023, ASME Journal of Heat and Mass Transfer)

   Abstract Drying of moist porous media can be very energy inefficient. For example, in the pulp and paper industry, paper drying consumes more than two-thirds of the total energy used in paper machines. Novel drying technologies can decrease the energy used for drying and lessen the manufacturing processes' carbon footprint. Developing next-generation drying technologies to dry moist porous media may require an understanding of removing moisture from a fully saturated porous material with excess water. This paper provides a fundamental understanding of heat and mass transfer in a fully saturated porous medium with excess water. This is relevant, for example, in drying tissue as well as pulp or paper for the purpose of thermal insulation where pressing is preferred to be avoided to overcome the reduction in the sheet thickness. For this purpose, a theoretical drying model is developed where the porous medium corresponds to paper and is assumed to be sandwiched between two excess-water layers (bottom and top). The conjugate model consists of energy and mass conservation equations for each layer. The model is validated with corresponding experimental data. In the model, the thickness of each water layer is calculated as a function of drying time based on local temperature and total moisture content. The numerical model is transient and one-dimensional in space (i.e., in the thickness direction). This paper demonstrates the governing equations, boundary conditions, and results when the saturated porous medium with water layers is heated from one side. Moisture and temperature profiles are estimated in the thickness direction of the porous medium as it dries. 

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2. A Machine Learning Tutorial for Operational Meteorology. Part II: Neural Networks and Deep Learning

   https://doi.org/10.1175/WAF-D-22-0187.1  

   Chase, Randy J.; Harrison, David R.; Lackmann, Gary M.; McGovern, Amy (August 2023, Weather and Forecasting)

   Abstract Over the past decade the use of machine learning in meteorology has grown rapidly. Specifically neural networks and deep learning have been used at an unprecedented rate. To fill the dearth of resources covering neural networks with a meteorological lens, this paper discusses machine learning methods in a plain language format that is targeted to the operational meteorological community. This is the second paper in a pair that aim to serve as a machine learning resource for meteorologists. While the first paper focused on traditional machine learning methods (e.g., random forest), here a broad spectrum of neural networks and deep learning methods is discussed. Specifically, this paper covers perceptrons, artificial neural networks, convolutional neural networks, and U-networks. Like the Part I paper, this manuscript discusses the terms associated with neural networks and their training. Then the manuscript provides some intuition behind every method and concludes by showing each method used in a meteorological example of diagnosing thunderstorms from satellite images (e.g., lightning flashes). This paper is accompanied with an open-source code repository to allow readers to explore neural networks using either the dataset provided (which is used in the paper) or as a template for alternate datasets. 

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3. A New Paper-Based Microfluidic Device for Improved Detection of Nitrate in Water

   https://doi.org/10.3390/s21010102  

   Charbaji, Amer; Heidari-Bafroui, Hojat; Anagnostopoulos, Constantine; Faghri, Mohammad (January 2021, Sensors)

   null (Ed.)

   In this paper, we report a simple and inexpensive paper-based microfluidic device for detecting nitrate in water. This device incorporates two recent developments in paper-based technology suitable for nitrate detection and has an optimized microfluidic design. The first technical advancement employed is an innovative fibrous composite material made up of cotton fibers and zinc microparticles that can be incorporated in paper-based devices and results in better nitrate reduction. The second is a detection zone with an immobilized reagent that allows the passage of a larger sample volume. Different acids were tested—citric and phosphoric acids gave better results than hydrochloric acid since this acid evaporates completely without leaving any residue behind on paper. Different microfluidic designs that utilize various fluid control technologies were investigated and a design with a folding detection zone was chosen and optimized to improve the uniformity of the signal produced. The optimized design allowed the device to achieve a limit of detection and quantification of 0.53 ppm and 1.18 ppm, respectively, for nitrate in water. This accounted for more than a 40% improvement on what has been previously realized for the detection of nitrate in water using paper-based technology. 

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4. Artifact - Semantic Analysis of Macro Usage for Portability

   https://doi.org/10.5281/zenodo.7783131  

   Brent; Paul (January 2024, Zenodo)

   This is the artifact abstract for the ICSE 2024 paper, *Semantic Analysis of Macro Usage for Portability*. This artifact provides the source code of Maki, the tool described in the paper, and instructions on how to run Maki to replicate the results originally reported in the paper. The paper's original results are also included so that one may cross-reference them against the results they obtain while attempting to replicate them. We claim the **available** and **reusable** badges. We believe this artifact deserves the available badge because it is publicly available on Zenodo at https://doi.org/10.5281/zenodo.7783131 (DOI 10.5281/zenodo.7783131). We believe this artifact deserves the reusable badge because it includes instructions for reproducing all the paper's major results, along with a dataset one may verify them against. This artifact also utilizes Docker to facilitate reuse, as recommended in the ICSE 2024 Call for Artifact Submissions. A reviewer who wishes to evaluate this artifact must be familiar with Docker and the Linux command line. Clang and Python experience is advised, but not essential. A reviewer will need Docker to run the artifact, and should have a device with at least 8 threads and 8GB of RAM. "Kicking the tires" and replicating a portion of the paper's original results should take about 20 minutes of time and 2GB of storage memory. Replicating the paper's full results would require over two weeks of time and 620GB of storage memory. The artifact does not require any specific operating system or environment to run. 

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5. A Fully-Papertronic Biosensing Array for High-Throughput Characterization of Microbial Electrogenicity

   https://doi.org/10.1109/EMBC.2018.8513677  

   Tahernia, Mehdi; Mohammadifar, Maedeh; Hassett, Daniel J.; Choi, Seokheun (July 2018, Conf Proc IEEE Eng Med Biol Soc. 2018)

   For the first time, we report a low-cost, disposable fully-papertronic screening platform for rapid screening and identification of electroactive microorganisms. This novel papertronic device is capable of simultaneous characterizing the electrogenicity of 10’s of the newly discovered, genetically engineered, bacteria. This work explored an exciting range of possibilities with the goal of fusing microbial fuel cell technology with ‘papertronics,’ the emerging field of paper-based electronics. Spatially distinct 64 sensing units of the array were constructed by patterning hydrophilic anodic reservoirs in paper with hydrophobic wax boundaries and utilizing 3-D multi-laminate paper structures. Full integration of a high-performance microbial sensor on paper can be achieved by improving the microbial electron exchange with the electrodes in an engineered conductive paper reservoir and reducing cathodic overpotential by using a solid electron acceptor on paper. Furthermore, the intrinsic capillary force of the paper and the increased capacity from the engineered reservoir allowed for rapid adsorption of the bacterial sample and promote immediate microbial cell attachment to the electrode, leading to instant power generation with even a small amount of the liquid. 

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