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1. Sensitivity Analysis of a Calibrated Data Center Model to Minimize the Site Survey Effort

   Singh, Saurabh; Nemati, Kourosh; Simon, Vibin; Siddarth, Ashwin; Seymour, Mark; Agonafer, Dereje (January 2021, 2021 37th Semiconductor Thermal Measurement, Modeling & Management Symposium (SEMI-THERM))

   null (Ed.)

   To reproduce a Digital Twin (DT) of a data center (DC), input data is required which is collected through site surveys. Data collection is an important step since accurate representation of a DC depends on capturing the necessary detail for various model fidelity levels of each DC component. However, guidance is lacking in this regard as to which components within the DC are crucial to achieve the level of accuracy desired for the computational model. And determining the input values of the component object parameters is an exercise in engineering judgement during site survey. Sensitivity analysis can be an effective methodology to determine how the level of simplification in component models can affect the model accuracy.In this study, a calibrated raised-floor DC model is used to study the sensitivity of a DC component's representation to the DC model accuracy. Commercial CFD tool, 6SigmaDC Room is used for modeling and simulation. A total of 8 DC components are considered and eventually ranked on the basis of time and effort required to collect model input data. For parametrized component object, the object's full range of input parameter values are considered, and simulations run. The results are compared with the baseline calibrated model to understand the trade-off between survey effort/cost and model accuracy. For the calibrated DC model and of the 8 components considered, it was observed that the chilled water piping branches, data cables and the cable penetration seal (found within cabinets) have considerable influence on the tile flow rate prediction accuracy. 

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2. Fibro‐Gel: An All‐Aqueous Hydrogel Consisting of Microfibers with Tunable Release Profile and its Application in Wound Healing

   https://doi.org/10.1002/adma.202211637  

   Shen, Yanting; Liu, Yuan; Nunes, Janine K; Wang, Chenmin; Xu, Miao; To, Michael KT; Stone, Howard A; Shum, Ho Cheung (May 2023, Advanced Materials)

   Abstract Injectable hydrogels are valuable tools in tissue engineering and regenerative medicine due to their unique advantages of injectability with minimal invasiveness and usability for irregularly shaped sites. However, it remains challenging to achieve scalable manufacturing together with matching physicochemical properties and on‐demand drug release for a high level of control over biophysical and biomedical cues to direct endogenous cells. Here, the use of an injectable fibro‐gel is demonstrated, a water‐filled network of entangled hydrogel microfibers, whose physicochemical properties and drug release profiles can be tailored to overcome these shortcomings. This fibro‐gel exhibits favorable in vitro biocompatibility and the capability to aid vascularization. The potential use of the fibro‐gel for advancing tissue regeneration is explored with a mice excision skin model. Preliminary in vivo tests indicate that the fibro‐gel promotes wound healing and new healthy tissue regeneration at a faster rate than a commercial gel. Moreover, it is demonstrated that the release of distinct drugs at different rates can further accelerate wound healing with higher efficiency, by using a two‐layer fibro‐gel model. The combination of injectability and tailorable properties of this fibro‐gel offers a promising approach in biomedical fields such as therapeutic delivery, medical dressings, and 3D tissue scaffolds for tissue engineering. 

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3. AC Power Flow Informed Parameter Learning for DC Power Flow Network Equivalents

   https://doi.org/10.1109/TPEC60005.2024.10472173  

   Taheri, Babak; Molzahn, Daniel K (February 2024, IEEE)

   This paper presents an algorithm to optimize the parameters of power systems equivalents to enhance the accuracy of the DC power flow approximation in reduced networks. Based on a zonal division of the network, the algorithm produces a reduced power system equivalent that captures inter-zonal flows with aggregated buses and equivalent transmission lines. The algorithm refines coefficient and bias parameters for the DC power flow model of the reduced network, aiming to minimize discrepancies between inter-zonal flows in DC and AC power flow results. Using optimization methods like Broyden-Fletcher-Goldfarb-Shanno (BFGS), Limited-memory BFGS (L-BFGS), and Truncated Newton Conjugate-Gradient (TNC) in an offline training phase, these parameters boost the accuracy of online DC power flow computations. In contrast to existing network equivalencing methods, the proposed algorithm optimizes accuracy over a specified range of operation as opposed to only considering a single nominal point. Numerical tests demonstrate substantial accuracy improvements over traditional equivalencing and approximation methods. 

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4. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning

   https://doi.org/10.1038/s41592-023-01940-w  

   Chklovski, Alex; Parks, Donovan H; Woodcroft, Ben J; Tyson, Gene W (August 2023, Nature Methods)

   Advances in sequencing technologies and bioinformatics tools have dramatically increased the recovery rate of microbial genomes from metagenomic data. Assessing the quality of metagenome-assembled genomes (MAGs) is a critical step before downstream analysis. Here, we present CheckM2, an improved method of predicting genome quality of MAGs using machine learning. Using synthetic and experimental data, we demonstrate that CheckM2 outperforms existing tools in both accuracy and computational speed. In addition, CheckM2’s database can be rapidly updated with new high-quality reference genomes, including taxa represented only by a single genome. We also show that CheckM2 accurately predicts genome quality for MAGs from novel lineages, even for those with reduced genome size (for example, Patescibacteria and the DPANN superphylum). CheckM2 provides accurate genome quality predictions across bacterial and archaeal lineages, giving increased confidence when inferring biological conclusions from MAGs. 

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5. Evaluating the Drought Code for lowland taiga of Interior Alaska using eddy covariance measurements

   https://doi.org/10.1071/WF22165  

   Miller, Eric A; Iwata, Hiroki; Ueyama, Masahito; Harazono, Yoshinobu; Kobayashi, Hideki; Ikawa, Hiroki; Busey, Robert; Iwahana, Go; Euskirchen, Eugénie S (January 2023, International Journal of Wildland Fire)

   Background The Drought Code (DC) of the Canadian Fire Weather Index System (CFWIS) has been intuitively regarded by fire managers in Alaska, USA, as poorly representing the moisture content in the forest floor in lowland taiga forests on permafrost soils. Aims The aim of this study was to evaluate the DC using its own framework of water balance as cumulative additions of daily precipitation and substractions of actual evaporation. Methods We used eddy covariance measurements (EC) from three flux towers in Interior Alaska as a benchmark of natural evaporation. Key results The DC water balance model overpredicted drought for all 14 site-years that we analysed. Errors in water balance cumulated to 109 mm by the end of the season, which was 54% of the soil water storage capacity of the DC model. Median daily water balance was 6.3 times lower than that measured by EC. Conclusions About half the error in the model was due to correction of precipitation for canopy effects. The other half was due to dependence of the actual evaporation rate on the proportional ‘fullness’ of soil water storage in the DC model. Implications Fire danger situational awareness is improved by ignoring the DC in the CFWIS for boreal forests occurring on permafrost. 

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