INJEX system

Development and evaluation of a health-based heat wave categorization system: a case study of Seville, Spain.

Kalkstein Laurence S LS, Sheridan Scott S, Martín-Olalla José-María JM, Berg Elizabeth E et al.

In the summer of 2022, our team of climatologists and researchers piloted the first heat warning system that ranks heat events based upon human health outcomes. This system uses local all-cause mortality data in addition to meteorological factors including air mass identification and the Excess Heat Factor to identify heat events that are likely to endanger human health in the region and applies three categories representing increasing severity. In this study, we evaluated this system by comparing the heat events it identified in Seville, Spain between 1995 and 2022 with those identified by the Spanish national meteorology agency (AEMET). We also compared the outputs from both systems against all-cause mortality data over the same time period. We found that our system captured more dangerous heat events than the meteorology-only criteria of AEMET and that the majority of identified heat events occurred during periods of elevated mortality. Additionally, the agreement between systems differed by event severity; roughly half of the high mortality "Category 3" events identified by our system were also identified by AEMET, but AEMET only issued alerts during one of the 24 more minor "Category 1" events that our system identified, even though these events were also correlated with increased mortality. Overall, these results show the potential of health-based warning systems in identifying dangerous events that might be missed by traditional meteorology-based systems.

A model for predicting pointing time in an eye-gaze input system using three basic phases of cursor movement trajectories.

Murata Atsuo A, Doi Toshihisa T, Karwowski Waldemar W

Few attempts have been made to explain pointing time in an eye-gaze input system as a multi-part model for different movement phases. To improve the prediction accuracy of pointing time for an eye-gaze input system, we proposed a model to predict pointing time based on the observation of cursor movement trajectories from the start until the completion of pointing movement. The cursor movement process was classified into three phases: (i) latency response phase, (ii) ballistic eye movement phase, and (iii) homing eye movement phase. In phase (i), the relation was not affected by target size and movement distance, but was influenced by movement direction. The relation between the distance to a target center and the duration in phase (ii) was not affected by movement direction and target size. In phase (iii), the relation was affected by both target size and movement direction. Using the identified characteristics above, the model to predict the duration of each phase was proposed. Compared with the traditional Fitts' model, the proposed model resulted in an improved and satisfactory prediction accuracy of pointing time (contribution of more than 0.9) for an eye-gaze input system. The proposed model is expected to contribute to build up an effective and reliable model to predict pointing time necessary for the design and evaluation of human-computer interface (HCI) in an eye-gaze system.

Management of Pregnancy in Type 1 Diabetes Using a Novel Automated Insulin Delivery System.

Bhasin Kanchan K, Bowdler Marissa M, Scifres Christina M CM, Cleary Erin M EM et al.

FPGA-based reconfigurable scanning and data acquisition system for scanning electron microscopy.

Gong Kyubin K, Kim Junseok J, Kim Bog G BG, Cho Boklae B

We present an FPGA-based reconfigurable scanning and data acquisition system for scanning electron microscopy (SEM). Built on the Xilinx Artix-7 (XC7A35T), the system integrates dual-channel 14-bit DAC raster scan waveform generation, dual-channel 12-bit ADC signal acquisition with on-chip averaging, and real-time USB 2.0 High-Speed data streaming at up to 40 MB/s. Integration with a commercial SEM (ModuleSci PicoEye-100) produced clearly resolved secondary-electron images, demonstrating stable raster operation in the fast-scan mode used for alignment and focusing. Standard data acquisition was performed at a per-frame acquisition time of 10 s, and a quantitative image-quality benchmark against the instrument's built-in acquisition channel under this condition, using a grid-hole masking protocol and sub-pixel cross-correlation drift correction (Guizar-Sicairos et al. 2008), demonstrates substantial SNR improvements. The FPGA-based system achieves 41-47% higher spatial SNR and near-theoretical temporal SNR scaling, reaching a [Formula: see text] improvement over the commercial reference. These results highlight the effectiveness of hardware-level synchronization for improving the practical recoverability of high-frequency spatial detail under reduced acquisition time. The modular architecture is applicable to a broad range of point-scanning instruments beyond electron microscopy.