Exploring Parents\u27 Knowledge of Dark Design and Its Impact on Children\u27s Digital Well-Being

Dark design (also known as deceptive design; Colin et al., 2018 and dark patterns; Mathur et al., 2019) is evidenced by “a user interface carefully crafted to trick users into doing things they might not otherwise do” (Brignull, 2022; page 1). Much dark design is constructed with monetization as the primary goal- even in spaces without ecommerce design (e.g., free-to-play apps representing \u3e95% of all mobile apps; Fitton et al. 2021). Many recent dark design strategies are also oriented towards collecting user information. Concerns about children’s vulnerability to inappropriate online marketing and economic fraud, and the impact of organisational data collection upon children’s privacy are increasing (European Commission, 2022; OECD, 2011; OFCOM, 2022). Regulators have begun to recognize, challenge, and fine deceptive design practices aimed at children (e.g., $245 million Epic Games settlement; FTC 2022), however, the scope and extent of dark design practices is such that regulators alone cannot safeguard children from such practices. Parents, who are widely understood to be primarily responsible for children’s online experiences, and children themselves, need to be mindful of and resistant to dark design practices in online spaces. With this in mind, this paper explores the following questions: (a) What is the influence of dark design (1) across mediums (e.g., apps, video games, social media platforms, websites) and (2) across differently-aged children? (b) To what extent are parents aware of their children’s exposure to dark design and the risks such exposure poses? (c) How effective are marketplace and regulatory controls

Sharenting in an Evolving Digital World: Increasing Online Connection and Consumer Vulnerability

Sharenting (using social media to share content about one’s child) is a progressively common phenomenon enabled by society’s increased connection to digital technology. Although it can facilitate positive connections among internet users, it also leads to concerns related to children’s online privacy and well-being. This paper establishes boundaries and terminology related to sharenting in an evolving digital world. First, while much of the prior sharenting research focuses on the parent-child dyad, we apply consumer vulnerability theory to conceptualize a modern sharenting ecosystem involving key stakeholders (parents, children, community, commercial institutions, and policymakers). This expanded ecosystem extends the sharenting literature to better define the complex dynamism inherent in sharenting as part of our increasingly virtual society. Next, we expand the characterization of sharenting by introducing a spectrum of awareness that categorizes types of sharenting (active, passive, and invisible). These definitions help researchers explore how and why personally identifiable information (PII) may be (un)intentionally shared in different contexts. Finally, using the conceptual structures developed in this paper, we provide a research agenda for policymakers and consumer welfare researcher

Prevalence of JC Virus in Chinese Patients with Colorectal Cancer

BACKGROUND: JCV is a DNA polyomavirus very well adapted to humans. Although JCV DNA has been detected in colorectal cancers (CRC), the association between JCV and CRC remains controversial. In China, the presence of JCV infection in CRC patients has not been reported. Here, we investigated JCV infection and viral DNA load in Chinese CRC patients and to determine whether the JCV DNA in peripheral blood (PB) can be used as a diagnostic marker for JCV-related CRC. METHODOLOGY/PRINCIPAL FINDINGS: Tumor tissues, non-cancerous tumor-adjacent tissues and PB samples were collected from 137 CRC patients. In addition, 80 normal colorectal tissue samples from patients without CRC and PB samples from 100 healthy volunteers were also harvested as controls. JCV DNA was detected by nested PCR and glass slide-based dot blotting. Viral DNA load of positive samples were determined by quantitative real-time PCR. JCV DNA was detected in 40.9% (56/137) of CRC tissues at a viral load of 49.1 to 10.3×10(4) copies/µg DNA. Thirty-four (24.5%) non-cancerous colorectal tissues (192.9 to 4.4×10(3) copies/µg DNA) and 25 (18.2%) PB samples (81.3 to 4.9×10(3) copies/µg DNA) from CRC patients were positive for JCV. Tumor tissues had higher levels of JCV than non-cancerous tissues (P = 0.003) or PB samples (P<0.001). No correlation between the presence of JCV and demographic or medical characteristics was observed. The JCV prevalence in PB samples was significantly associated with the JCV status in tissue samples (P<0.001). Eleven (13.8%) normal colorectal tissues and seven (7.0%) PB samples from healthy donors were positive for JCV. CONCLUSIONS/SIGNIFICANCE: JCV infection is frequently present in colorectal tumor tissues of CRC patients. Although the association between JCV presence in PB samples and JCV status in tissue samples was identified in this study, whether PB JCV detection can serve as a marker for JCV status of CRC requires further study

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

JUNO Sensitivity to Invisible Decay Modes of Neutrons

We explore the bound neutrons decay into invisible particles (e.g., $n\rightarrow 3 \nu$ or $nn \rightarrow 2 \nu$) in the JUNO liquid scintillator detector. The invisible decay includes two decay modes: $n \rightarrow { inv}$ and $nn \rightarrow { inv}$. The invisible decays of $s$-shell neutrons in $^{12}{\rm C}$ will leave a highly excited residual nucleus. Subsequently, some de-excitation modes of the excited residual nuclei can produce a time- and space-correlated triple coincidence signal in the JUNO detector. Based on a full Monte Carlo simulation informed with the latest available data, we estimate all backgrounds, including inverse beta decay events of the reactor antineutrino $\bar{\nu}_e$, natural radioactivity, cosmogenic isotopes and neutral current interactions of atmospheric neutrinos. Pulse shape discrimination and multivariate analysis techniques are employed to further suppress backgrounds. With two years of exposure, JUNO is expected to give an order of magnitude improvement compared to the current best limits. After 10 years of data taking, the JUNO expected sensitivities at a 90% confidence level are $\tau/B( n \rightarrow { inv} ) > 5.0 \times 10^{31} \, {\rm yr}$ and $\tau/B( nn \rightarrow { inv} ) > 1.4 \times 10^{32} \, {\rm yr}$.Comment: 28 pages, 7 figures, 4 table