The State University of New York at Buffalo commonly referred to as University at Buffalo (UB) or SUNY Buffalo, is a public research university with campuses in Buffalo and Amherst, New York. The university was founded in 1846 as a private medical college and merged with the State University of New York system in 1962. As of Fall 2018, the university enrolls 31,503 students in 13 colleges, making it the largest public university in the state of New York.Since its founding by United States President Millard Fillmore, the university has evolved from a small medical school to a large research university. Today, in addition to the College of Arts and Sciences, the university houses the largest state-operated medical school, dental school, education school, business school, engineering school, and pharmacy school, and is also home to New York’s only state-operated law school. UB has the largest enrollment, largest endowment, and most research funding among the universities in the SUNY system. The university offers bachelor’s degrees in over 100 areas of study, as well as 205 master's degrees, 84 doctoral degrees, and 10 professional degrees. The University at Buffalo, along with the University of Virginia, are the only colleges founded by United States Presidents.The University at Buffalo is classified as an R1 University, meaning that it engages in a very high level of research activity. In 1989, UB was elected to the Association of American Universities, a selective group of major research universities in North America. UB's alumni and faculty have included five Nobel laureates, five Pulitzer Prize winners, one Prime Minister of Somalia, two astronauts, three billionaires, one Academy Award winner, one Emmy Award winner, and Fulbright Scholars.The University at Buffalo intercollegiate athletic teams are the Bulls. They compete in Division I of the NCAA, and are members of the Mid-American Conference.
PurposeExtended reality (XR) technologies (i.e. augmented reality [AR] and virtual reality [VR]) have changed the digital landscape in recent years and present novel opportunities to marketers for providing innovative, experiential benefits to consumers. Many firms have developed and announced XR experiences (XREs); however, no prior work has assessed how investors react to these offerings and whether firms are rewarded for specific strategic decisions over others. This study aims to address this gap.Design/methodology/approachThe authors collected a comprehensive list of announcements of XREs made by publicly traded US firms. This study's data set consists of 179 announcements made during the period of 2010 through 2022. The authors then conducted an event study to assess the effect of these announcements on firm value (i.e. stock price). In addition, the authors ran a moderation analysis to uncover which strategic decisions lead some firms to witness different financial effects than others.FindingsOn average, firms witness a 0.38% (equivalent to $437m) loss in firm value after announcing XREs. However, this study's moderation analysis reveals that firms that choose to adopt AR rather than VR, focus on the purchase phase of the customer journey (rather than pre-purchase or post-purchase) and/or announced XREs in more recent years, witness an increase in firm value.Research limitations/implicationsOwing to financial data availability constraints, the event study focused on publicly traded firms in the USA. Future work should be expanded to international contexts, as scholars have suggested that XR technologies may face weaker acceptance in countries that are less individualistic and more collectivistic.Practical implicationsThe findings of this work allow firms to understand how to approach XREs in a way that enhances the consumer experience and is ultimately rewarded by investors. This event study suggests that firms should take a cautiously optimistic approach to XREs.Originality/valueThis research applies vividness theory to understand how the consumer experience is affected by heterogeneous decisions that firms make when developing and announcing XREs, which, in turn, can affect investors' responses. It also provides actionable strategic advice for practitioners who are interested in effectively approaching XR technologies. In addition, this study outlines important directions for future research within this domain.
Research on racial disparities in the criminal legal system generally examines isolated sentencing decisions, rather than the "package" of punishment that defendants experience. Using Minnesota court administrative data from 2004 to 2017, we specify multivariate and instrumental variables models to simultaneously estimate the outcomes of three elements of racialized punishment: incarceration, probation, and monetary sanctions. We instrument incarceration using jail capacity, which accounts for confounding and the simultaneity of incarceration and other punishment forms. Our results show racial patterning in the "mix" of punishment for similarly situated defendants. Before accounting for this mix, Black, Hispanic, and Native American defendants appear to receive less probation and lower monetary sanctions, but longer incarceration than White defendants. After accounting for instrumented incarceration, monetary sanctions and probation are racialized beyond incarceration in complex ways: Black, Hispanic, and Native American defendants receive lower monetary sanctions as compared to White defendants, and probation for Black and Native American defendants is higher after adjustment. The contours of this racialized package depend critically upon whether the state guidelines recommend a prison sentence. These results show that punishment can be modeled as experienced-as a constitutive package of costs, surveillance, and confinement constrained by structural features of state sentencing guidelines.
We demonstrate that a $\sim 2 \sigma$ discrepancy with the Planck-$\Lambda$CDM cosmology in DESI Luminous Red Galaxy (LRG) data at $z_{\textrm{eff}} = 0.51$ translates into an unexpectedly large $\Omega_m$ value, $\Omega_m = 0.668^{+0.180}_{-0.169}$. We independently confirm that this anomaly drives the preference for $w_0 > -1$ in DESI data confronted to the $w_0 w_a$CDM model. We show that redshift bins of DESI constraints allow $\Omega_m$ to wiggle at the $\sim 2 \sigma$ level with increasing effective redshift in the $\Lambda$CDM model. Given that LRG data at $z_{\textrm{eff}} = 0.51$ is at odds with Type Ia supernovae in overlapping redshifts, we expect that this anomaly will decrease in statistical significance with future DESI data releases leaving an increasing $\Omega_m$ trend with effective redshift at higher redshifts. We estimate the significance of the latter in DESI data at $\sim 1.8 \sigma$ and comment on how it dovetails with independent observations. It is imperative to understand what makes DESI LRG data at $z_{\textrm{eff}} = 0.51$ an outlier when it comes to $\Omega_m$ determinations.
Sample consumption for serial femtosecond crystallography with X-ray free electron lasers remains a major limitation preventing broader use in macromolecular crystallography. This drawback is exacerbated in time-resolved (TR) experiments, where the amount of sample required per reaction time point is multiplied by the number of time points investigated. To reduce this limitation, we demonstrate a segmented droplet generation strategy coupled to a mix-and-inject approach for TR studies at the European XFEL. The injector produces synchronized droplet trains that enable stable and reproducible injection of protein crystal slurries at significantly reduced flow rates. Using the human flavoenzyme NAD(P)H:quinone oxidoreductase 1 (NQO1) as a test system, we collected diffraction data after mixing with NADH at 0.3 s and 1.2 s delays. The segmented injection approach achieved up to 97% reduction in sample consumption compared with continuous-flow injection while maintaining data quality suitable for TR crystallography. Reproducible electron density features consistent with low-occupancy NADH binding illustrate both the feasibility and the current limits of studying dynamic redox enzymes using this approach. This work establishes segmented droplet generation as a sample-efficient and XFEL-compatible method for future time-resolved serial crystallography experiments.
We wish to highlight a long-neglected issue in nanofilm composite (NFC) membranes for gas separation: selective nanofilms (<100 nm) exhibit markedly different gas separation properties from bulk films (>10 mu m) due to nanoconfinement and interfacial interactions with substrates. We synthesize three series of poly(ethylene glycol) (PEG)-based copolymers with excellent intrinsic CO2/N-2 separation properties. When they are fabricated into NFC membranes with selective layers of 15-95 nm, CO2 permeability decreases dramatically owing to nanoconfinement and the affinity between the copolymers and dopamine-modified gutter layer, while CO2/N-2 selectivity remains similar. For example, a copolymer (PEGDA5) synthesized from 95% PEG methyl ether acrylate (PEGMEA) and 5% PEG diacrylate (PEGDA) exhibits CO2 permeability of 420 Barrer for bulk films but only 120 Barrer for a 95 nm layer. Nevertheless, the membrane exhibits CO2 permeance of 1570 GPU and CO2/N-2 selectivity of 52 at 25 degrees C, comparable to state-of-the-art membranes and surpassing Robeson's upper bound. The nanoscale behaviors elucidated in this study should be useful for designing NFC membranes for important gas separations.
