In this paper, we performed an experiment with activated carbon manufactured from waste coffee grounds on the compressive strength of normal cement mortars. The activated carbon reinforcement was manufactured from waste coffee grounds, and the collected coffee grounds were then transformed into activated carbon granules through the physical activation process. The activated carbon/cement composites were prepared by mixing cement with activated carbon granules with the weight fractions of 0.5%, 1%, 1.5%, 5%, and 10% cement. The experimental results show that adding activated carbon up to 1.5 wt% increased the early strength of cement mortars. Furthermore, we found that the composites incorporated with a small amount of activated carbon (≤1.5 wt%) had higher compressive strength over the curing period than the normal cement without activated carbon. We believe that these results would potentially have commonalities with morphological symmetry phenomena that occur on the surfaces of activated carbon granules.

Activated carbon can be manufactured from waste coffee grounds via physical and/or chemical activation processes. However, challenges remain to quantify the differences in surface morphology between manufactured activated carbon granules and the waste coffee grounds. This paper presents a novel quantitative method to determine the quality of the physical and chemical activation processes performed in the presence of intensity inhomogeneity and identify surface characteristics of manufactured activated carbon granules and the waste coffee grounds. The spatial density was calculated by the Getis-Ord-Gi* statistic in scanning electron microscopy images. The spatial characteristics were determined by analyzing Ripley’s K function and complete spatial randomness. Results show that the method introduced in this paper is capable of distinguishing between manufactured activated carbon granules and the waste coffee grounds, in terms of surface morphology.

<div>Abstract<p>Whole-slide histology images contain information that is valuable for clinical and basic science investigations of cancer but extracting quantitative measurements from these images is challenging for researchers who are not image analysis specialists. In this article, we describe HistomicsML2, a software tool for learn-by-example training of machine learning classifiers for histologic patterns in whole-slide images. This tool improves training efficiency and classifier performance by guiding users to the most informative training examples for labeling and can be used to develop classifiers for prospective application or as a rapid annotation tool that is adaptable to different cancer types. HistomicsML2 runs as a containerized server application that provides web-based user interfaces for classifier training, validation, exporting inference results, and collaborative review, and that can be deployed on GPU servers or cloud platforms. We demonstrate the utility of this tool by using it to classify tumor-infiltrating lymphocytes in breast carcinoma and cutaneous melanoma.</p>Significance:<p>An interactive machine learning tool for analyzing digital pathology images enables cancer researchers to apply this tool to measure histologic patterns for clinical and basic science studies.</p></div>

<p>Software preview video.</p>

<p>Supplementary tables.</p>