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The Front Cover illustrates ultrafast spectroscopic insights into the photoexcited energy relaxation pathways of St. John's wort-derived fluorescent photosensitizer hypericin in solution. The bidirectional excited-state intramolecular proton transfer (ESIPT) gains prominence after UV excitation with enhanced photoprotection in a “proton pachinko”, whereas visible excitation results in more phototoxicity. More information can be found in the Research Article by C. Fang and co-workers (DOI: 10.1002/chem.202500639). Cover design by S. Johnson and C. Fang. 

The COVID-19 pandemic was a catalyst for many different trends in our daily life worldwide. While there has been an overall rise in cybercrime during this time, there has been relatively little research done about malicious COVID-19 themed AndroidOS applications. With the rise in reports of users falling victim to malicious COVID-19 themed AndroidOS applications, there is a need to learn about the detection of malware for pandemics-themed mobile apps.. In this project, we extracted the permissions requests from 1959 APK files from a dataset containing benign and malware COVID-19 themed apps. We then created and compared eight unique models of four varying classifiers to determine their ability to identify potentially malicious APK files based on the permissions the APK file requests: support vector machine, neural network, decision trees, and categorical naive bayes. These classifiers were then trained using Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset due to the lack of samples of malware compared to non-malware APKs. Finally, we evaluated the models using K-Fold Cross-Validation and found the decision tree classifier to be the best performing classifier. 

We, as a society, need artists to help us interpret and explain science, but what does an artist's studio look like when today's science is built upon the language of large, increasingly complex data? This paper presents a data visualization design interface that lifts the barriers for artists to engage with actively studied, 3D multivariate datasets. To accomplish this, the interface must weave together the need for creative artistic processes and the challenging constraints of real-time, data-driven 3D computer graphics. The result is an interface for a technical process, but technical in the way artistic printmaking is technical, not in the sense of computer scripting and programming. Using metaphor, computer graphics algorithms and shader program parameters are reimagined as tools in an artist's printmaking studio. These artistic metaphors and language are merged with a puzzle-piece approach to visual programming and matching iconography. Finally, artists access the interface using a web browser, making it possible to design immersive multivariate data visualizations that can be displayed in VR and AR environments using familiar drawing tablets and touch screens. We report on insights from the interdisciplinary design of the interface and early feedback from artists. 

As scientific data grow larger and more complex, an equally rich visual vocabulary is needed to fully articulate its insights. We present a series of images that are made possible by a recent technical development “Artifact-Based Rendering,” a component of our broader effort to create a methodology for scientific visualization that draws on principles of art and design. 

We introduce Artifact-Based Rendering (ABR), a framework of tools, algorithms, and processes that makes it possible to produce real, data-driven 3D scientific visualizations with a visual language derived entirely from colors, lines, textures, and forms created using traditional physical media or found in nature. A theory and process for ABR is presented to address three current needs: (i) designing better visualizations by making it possible for non-programmers to rapidly design and critique many alternative data-to-visual mappings; (ii) expanding the visual vocabulary used in scientific visualizations to depict increasingly complex multivariate data; (iii) bringing a more engaging, natural, and human-relatable handcrafted aesthetic to data visualization. New tools and algorithms to support ABR include front-end applets for constructing artifact-based colormaps, optimizing 3D scanned meshes for use in data visualization, and synthesizing textures from artifacts. These are complemented by an interactive rendering engine with custom algorithms and interfaces that demonstrate multiple new visual styles for depicting point, line, surface, and volume data. A within-the-research-team design study provides early evidence of the shift in visualization design processes that ABR is believed to enable when compared to traditional scientific visualization systems. Qualitative user feedback on applications to climate science and brain imaging support the utility of ABR for scientific discovery and public communication.