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This article is an exploratory analysis of the impact of the California Consumer Privacy Act (CCPA) on data breaches that result in exposing sensitive private data of consumers. The CCPA applies to large for-profit businesses that collect and disseminate personal information of Californian consumers. It provides for consumer rights and imposes notification and security requirements on businesses that collect private information. We analyzed how CCPA affects data breach notifications that are required by the state's Office of Auditor General, for the period 2012 to 2023. The analysis provides interesting insights into the impact of CCPA on the pattern of data breaches. Our principal finding is that privacy breaches reduced to some extent after CCPA. Importantly, CCPA has helped in the overall improvement in reporting privacy breaches. We surmise that the CCPA brought more data breaches into light. 

The widespread use of machine learning algorithms in radiomics has led to a proliferation of flexible prognostic models for clinical outcomes. However, a limitation of these techniques is their black-box nature, which prevents the ability for increased mechanistic phenomenological understanding. In this article, we develop an inferential framework for estimating causal effects with radiomics data. A new challenge is that the exposure of interest is latent so that new estimation procedures are needed. We leverage a multivariate version of partial least squares for causal effect estimation. The methodology is illustrated with applications to two radiomics datasets, one in osteosarcoma and one in glioblastoma. 

In nature, individual cells are compartmentalized by a membrane that protects the cellular elements from the surrounding environment while simultaneously equipped with an antioxidant defense system to alleviate the oxidative stress resulting from light, oxygen, moisture, and temperature. However, this mechanism has not been realized in cellular mimics to effectively encapsulate and retain highly reactive antioxidants. Here, we report cell-inspired hydrogel microcapsules with an interstitial oil layer prepared by utilizing triple emulsion drops as templates to achieve enhanced retention of antioxidants. We employ ionic gelation for the hydrogel shell to prevent exposure of the encapsulated antioxidants to free radicals typically generated during photopolymerization. The interstitial oil layer in the microcapsule serves as an stimulus-responsive diffusion barrier, enabling efficient encapsulation and retention of antioxidants by providing an adequate pH microenvironment until osmotic pressure is applied to release the cargo on-demand. Moreover, addition of a lipophilic reducing agent in the oil layer induces a complementary reaction with the antioxidant, similar to the nonenzymatic antioxidant defense system in cells, leading to enhanced retention of the antioxidant activity. Furthermore, we show the complete recovery and even further enhancement in antioxidant activity by lowering the storage temperature, which decreases the oxidation rate while retaining the complementary reaction with the lipophilic reducing agent. KEYWORDS: droplet microfluidics, cell-inspired microcapsule, encapsulation 

University-based makerspaces are receiving increasing attention as promising innovations that may contribute to the development of future engineers. Using a theory of social boundary spaces, we investigated whether the diverse experiences offered at university-based makerspaces may contribute to students’ learning and development of various “soft” or “21st century” skills that go beyond engineering-specific content knowledge. Through interviews with undergraduate student users at two university-based makerspaces in the United States we identified seven different types of boundary spaces (where multiple communities, and the individuals and activities affiliated with those communities, come together). We identified students engaging in the processes of identification, reflection, and coordination, which allowed them to make sense of, and navigate, the various boundary spaces they encountered in the makerspaces. These processes provided students with opportunities to engage with, and learn from, individuals and practices affiliated with various communities and disciplines. These opportunities can lead to students’ development of necessary skills to creatively and collaboratively address interdisciplinary socio-scientific problems. We suggest that university-based makerspaces can offer important developmental experiences for a diverse body of students that may be challenging for a single university department, program, or course to offer. Based on these findings, we recommend university programs and faculty intentionally integrate makerspace activities into undergraduate curricula to support students’ development of skills, knowledge, and practices relevant for engineering as well as 21st century skills more broadly. 

In the last decade, postsecondary institutions have seen a notable increase in makerspaces on their campuses and the integration of these spaces into engineering programs. Yet research into the efficacy of university-based makerspaces is sparse. We contribute to this nascent body of research in reporting on findings from a phenomenological study on the perceptions of faculty, staff, and students concerning six university-based makerspaces in the United States. We discuss the findings using a framework of heterogeneous engineering (integration of the social and technical aspects of engineering practice). Various physical, climate, and programmatic features of makerspaces were read as affordances for students’ development of engineering practices and their continued participation and persistence in engineering. We discuss the potential of makerspaces in helping students develop knowledge, skills, and proclivities that may support their attending to especially wicked societal problems, such as issues of sustainability. We offer implications for makerspace administrators, engineering program leaders, faculty, and staff, as well as those developing and delivering professional development for faculty and staff, to better incorporate makerspaces into the university engineering curriculum. 

Abstract Although using machine learning techniques to solve computer security challenges is not a new idea, the rapidly emerging Deep Learning technology has recently triggered a substantial amount of interests in the computer security community. This paper seeks to provide a dedicated review of the very recent research works on using Deep Learning techniques to solve computer security challenges. In particular, the review covers eight computer security problems being solved by applications of Deep Learning: security-oriented program analysis, defending return-oriented programming (ROP) attacks, achieving control-flow integrity (CFI), defending network attacks, malware classification, system-event-based anomaly detection, memory forensics, and fuzzing for software security.