Zinc oxide (ZnO) is considered to be one of the most explored and reliable sensing materials for UV detection due to its excellent properties, like a wide band gap and high exciton energy. Our current study on a photodetector based on tetrapodal ZnO (t-ZnO) reported an extremely high UV response of ~9200 for 394 nm UV illumination at 25 °C. The t-ZnO network structure and morphology were investigated using XRD and SEM. The sensor showed a UV/visible ratio of ~12 at 25 °C for 394 nm UV illumination and 443 nm visible illumination. By increasing the temperature, monotonic decreases in response and recovery time were observed. By increasing the bias voltage, the response time was found to decrease while the recovery time was increased. The maximum responsivity shifted to higher wavelengths from 394 nm to 400 nm by increasing the operating temperature from 25 °C to 100 °C. The t-ZnO networks exhibited gas-sensing performances at temperatures above 250 °C, and a maximum response of ~1.35 was recorded at 350 °C with a good repeatability and fast recovery in 16 s for 100 ppm of n-butanol vapor. This study demonstrated that t-ZnO networks are good biosensors that can be used for diverse biomedical applications like the sensing of VOCs (volatile organic compounds) and ultraviolet detection under a wide range of temperatures, and may find new possibilities in biosensing applications.

The p-value is a classical proposal of statistical inference, dating back to the seminal contributions by Fisher, Neyman and E. Pearson. However, p-values have been frequently misunderstood and misused in practice, and medical research is not an exception. In recent years, in several statistical and applied journals, a debate erupted about the need of clear guidelines in reporting p-values, which culminated with the publication of the ASA statement in 2016. In this paper, we assess strengths and limitations of p-values and we assert that in applied research the p-value should be supplemented by other measures, such as the Bayes factor, the Bayes false discovery rate and the local Bayes false discovery rate. We also review a recent proposal by Bayarri et al. from a Bayesian perspective that has the advantage of introducing an indicator, the rejection odds, which keeps into account both pre- and post-experimental information, and could also have a straightforward frequentist interpretation. We conduct a delimited numerical study that investigates on the relation of the Bayes factor with its maximum, and of the local Bayes false discovery rate with its minimum under different distributional assumptions and parameter choices. We illustrate the concepts expressed in theory with an example in clinical oncology, namely a randomized trial on the effectiveness of a new chemotherapy for patients with AIDS and Kaposi\'s sarcoma.