This paper presents a novel multilevel image segmentation method that leverages an enhanced Whale Optimization Algorithm (WOA). While WOA has shown promise in solving various optimization problems, its performance can be limited by susceptibility to local optima. To address this challenge, a Mixed-Strategy Improved Convergence WOA (MSICWOA) is proposed, which enhances the algorithm's optimization efficiency by incorporating a nonlinear convergence factor, an adaptive weight coefficient, and a k-point initialization technique. The MSICWOA is then applied alongside Otsu's crossvariance and Kapur entropy as objective functions to determine optimal thresholds for multilevel grayscale image segmentation. Experimental results on benchmark optimization functions demonstrate that MSICWOA outperforms traditional optimization methods in terms of both search accuracy and convergence speed, effectively overcoming local optima. Furthermore, image segmentation experiments on standard datasets validate the effectiveness of the MSICWOA-Kapur method in quickly and accurately identifying multilevel thresholds.
Background:Kocuria spp. are aerobic Gram-positive cocci and opportunistic pathogens. Although infections caused by Kocuria spp. have been reported, to the best of our knowledge, human infections caused by Kocuria indica have not been reported. Here, we report a case of pneumonia and bloodstream infection caused by K. indica. Case Summary:This is the first reported case of human K. indica infection. A 19-year-old female with a history of chronic idiopathic intestinal pseudo-obstruction presented with fever, cough, and malaise. Blood cultures revealed clusters of Gram-positive cocci. The organism was initially identified as Kocuria rhizophila using the VITEK MS system but was confirmed as K. indica using 16S rRNA gene sequencing. Conclusion:Although K. indica is a rare pathogen, it should be considered in cases of positive blood cultures. Genetic analysis, including 16S rRNA gene sequencing, is essential for the identification of this organism.
Women’s safety remains a critical global concern. Conventional panic applications and wearable devices require manual activation, which is often impossible when the victim is in shock, physically restrained, or under extreme stress. This paper proposes a semi-automatic women-safety mobile system that continuously monitors the user’s facial expressions using a lightweight Convolutional Neural Network (CNN). When a high probability of distress-related emotions (fear, anger, or sadness) is detected for three consecutive frames, the system instantly triggers strong haptic vibration and displays a large full-screen one-tap SOS confirmation button. Only if the user explicitly taps this button within 7 seconds does the system activate a loud deterrent siren and send the current GPS location along with a pre-recorded emergency message to pre-selected trusted contacts and, if the user has opted in during setup, to local emergency services. Experimental results on a combined dataset of approximately 50,000 facial images show a seven-class emotion classification accuracy of 89%. Real-world field trials conducted with 25 female volunteers in public environments recorded zero false or unintended emergency alerts, with an average time from first distress detection to confirmation screen appearance of 6.4 seconds and an average end-to-end alert transmission time of 6.4 seconds (including user confirmation). This is significantly faster than the 15–18 seconds required by traditional manual panic applications, while eliminating the risk of erroneous alerts that would occur in a fully automatic system. The proposed framework offers a practical, privacy-preserving, and ethically responsible solution that can be readily deployed on existing smartphones and wearable devices, contributing meaningfully to AI-driven personal safety technologies.
Since the 1960s, Grassy Narrows First Nation, Canada, has been exposed to methylmercury (MeHg) from fish consumption following Hg discharge from a chloralkali plant. Prenatal exposure to MeHg is known to affect the neurodevelopment of fetuses and the retina is sensitive to neurodevelopmental damage. The multidisciplinary, cross-sectional Niibin study, developed with Grassy Narrows First Nations, included visual examinations with retinal evaluation using optical coherence tomography (OCT). The present analyses focused on the 59 participants (116 eyes) with umbilical cord Hg measurements, sampled between 1971 and 1992. Associations between cord blood Hg and retinal thickness layers surrounding the optic nerve head (RNFL) and inner macula (GC-IPL) were examined using mixed-effect models. Higher cord blood Hg was significantly associated with reduced thickness of GC-IPL layers across all macular sectors; less pronounced associations were observed for RNFL. A qualitative clinical assessment of the OCT results showed that persons with cord blood Hg concentrations ≥ 5.8 µg/L were more likely to present bilateral abnormal retinal thinning (OR = 3.51; [95% CI: 1.06–11.53]). These findings suggest that, in this Indigenous community, prenatal MeHg exposure may have enduring effects on retinal thickness and underline the importance of OCT technology in providing tailored eye care.
