Polarization Conforms Performance Variability in Amorphous Electrodeposited Iridium Oxide pH Sensors: A Thorough Surface Chemistry Investigation.

Electrodeposited amorphous hydrated iridium oxide (IrOx) is a promising material for pH sensing due to its high sensitivity and the ease of fabrication. However, durability and variability continue to restrict the sensor's effectiveness. Variation in probe films can be seen in both performance and fabrication, but it has been found that performance variation can be controlled with potentiostatic conditioning (PC). To make proper use of this technique, the morphological and chemical changes affecting the conditioning process must be understood. Here, a thorough study of this material, after undergoing PC in a pH-sensing-relevant potential regime, was conducted by voltammetry, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). Fitting of XPS data was performed, guided by raw trends in survey scans, core orbitals, and valence spectra, both XPS and UPS. The findings indicate that the PC process can repeatably control and conform performance and surface bonding to desired calibrations and distributions, respectively; PC was able to reduce sensitivity and offset ranges to as low as ±0.7 mV/pH and ±0.008 V, respectively, and repeat bonding distributions over ~2 months of sample preparation. Both Ir/O atomic ratios (shifting from 4:1 to over 4.5:1) and fitted components assigned hydroxide or oxide states based on the literature (low-voltage spectra being almost entirely with suggested hydroxide components, and high-voltage spectra almost entirely with suggested oxide components) trend across the polarization range. Self-consistent valence, core orbital, and survey quantitative trends point to a likely mechanism of ligand conversion from hydroxide to oxide, suggesting that the conditioning process enforces specific state mixtures that include both theoretical Ir(III) and Ir(IV) species, and raising the conditioning potential alters the surface species from an assumed mixture of Ir species to more oxidized Ir species.

Developing an automatic warning system for anomalous chicken dispersion and movement using deep learning and machine learning.

Chicken is a major source of dietary protein worldwide. The dispersion and movement of chickens constitute vital indicators of their health and status. This is especially evident in Taiwanese native chickens (TNCs), a local variety which is high in physical activity when healthy. Conventionally, the dispersion and movement of chicken flocks are observed in patrols. However, manual patrolling is laborious and time-consuming. Moreover, frequent patrols increase the risk of carrying pathogens into chicken farms. To address these issues, this study proposes an approach to develop an automatic warning system for anomalous dispersion and movement of chicken flocks in commercial chicken farms. Embendded systems were developed to acquire videos of chickens from overhead view in a chicken house, in which approximately 20,000 TNCs were raised for a period of 10 wk. Each video was 5-min in length. The videos were transmitted to a remote cloud server and were converted into images. A You Only Look Once-version 7 tiny (YOLOv7-tiny) object detection model was trained to detect chickens in the images. The dispersion of the chicken flocks in a 5-min long video was calculated using nearest neighbor index (NNI). The movement of the chicken flocks in a 5-min long video was quantified using simple online and real-time tracking algorithm (SORT). The normal ranges (i.e., 95% confidence intervals) of chicken dispersion and movement were established using an autoregressive integrated moving average (ARIMA) model and a seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model, respectively. The system allows farmers to check up on the chicken farm only when the dispersion or movement values were not in the normal ranges. Thus, labor time can be saved and the risk of carrying pathogens into chicken farms can be reduced. The trained YOLOv7-tiny model achieved an average precision of 98.2% in chicken detection. SORT achieved a multiple object tracking accuracy of 95.3%. The ARIMA and SARIMAX achieved a mean absolute percentage error 3.71% and 13.39%, respectively, in forecasting dispersion and movement. The proposed approach can serve as a solution for automatic monitoring of anomalous chicken dispersion and movement in chicken farming, alerting farmers of potential health risks and environmental hazards in chicken farms.

Deep learning-based framework for cardiac function assessment in embryonic zebrafish from heart beating videos.

Zebrafish is a powerful and widely-used model system for a host of biological investigations, including cardiovascular studies and genetic screening. Zebrafish are readily assessable during developmental stages; however, the current methods for quantifying and monitoring cardiac functions mainly involve tedious manual work and inconsistent estimations. In this paper, we developed and validated a Zebrafish Automatic Cardiovascular Assessment Framework (ZACAF) based on a U-net deep learning model for automated assessment of cardiovascular indices, such as ejection fraction (EF) and fractional shortening (FS) from microscopic videos of wildtype and cardiomyopathy mutant zebrafish embryos. Our approach yielded favorable performance with accuracy above 90% compared with manual processing. We used only black and white regular microscopic recordings with frame rates of 5-20 frames per second (fps); thus, the framework could be widely applicable with any laboratory resources and infrastructure. Most importantly, the automatic feature holds promise to enable efficient, consistent, and reliable processing and analysis capacity for large amounts of videos, which can be generated by diverse collaborating teams.