Addressing heart failure (HF) as a prevalent global health concern poses difficulties in implementing innovative approaches for enhanced patient care. Predicting mortality rates in HF patients, in particular, is difficult yet critical, necessitating individualized care, proactive management, and enabling educated decision-making to enhance outcomes. Recently, the significance of voice biomarkers coupled with Machine Learning (ML) has surged, demonstrating remarkable efficacy, particularly in predicting heart failure. The synergy of voice analysis and ML algorithms provides a non-invasive and easily accessible means to evaluate patients' health. However, there is a lack of voice biomarkers for predicting mortality rates among heart failure patients with standardized speech protocols. Here, we demonstrate a powerful and effective ML model for predicting mortality rates in hospitalized HF patients through the utilization of voice biomarkers. By seamlessly integrating voice biomarkers into routine patient monitoring, this strategy has the potential to improve patient outcomes, optimize resource allocation, and advance patient-centered HF management. In this study, a Machine Learning system, specifically a logistic regression model, is trained to predict patients' 5-year mortality rates using their speech as input. The model performs admirably and consistently, as demonstrated by cross-validation and statistical approaches (p-value < 0.001). Furthermore, integrating NT-proBNP, a diagnostic biomarker in HF, improves the model's predictive accuracy substantially.

Interruptions in the supply chain during the coronavirus pandemic and problems in logistics that caused constant deviations from forecasts created significant problems for production-based economies. Production and logistics problems experienced at factories in China prompted Europe to turn to Turkey. When China and the U.S. moved containers to their own countries, Turkey started transporting them to Europe with trucks. At this stage, uninterrupted production necessitated technology-oriented transformation. This week, a Ventures60 event under the title "The Age of Uninterrupted Production" addressed a series of topics – from corporate intelligence solutions in the production of unmanned aerial vehicles used in Turkey's largest refinery, Tüpraş, to corporate investors investing in production-oriented artificial intelligence (AI) and cyberattack threats.

Rapid damage assessment is one of the core tasks that response organizations perform at the onset of a disaster to understand the scale of damage to infrastructures such as roads, bridges, and buildings. This work analyzes the usefulness of social media imagery content to perform rapid damage assessment during a real-world disaster. An automatic image processing system, which was activated in collaboration with a volunteer response organization, processed ~280K images to understand the extent of damage caused by the disaster. The system achieved an accuracy of 76% computed based on the feedback received from the domain experts who analyzed ~29K system-processed images during the disaster. An extensive error analysis reveals several insights and challenges faced by the system, which are vital for the research community to advance this line of research.

Istanbul, a city of 14 million people and a crossroads of cultural exchange dating back millennia, may also be where Turkey's next major earthquake strikes. Cities along the North Anatolian Fault, which stretches from eastern Turkey to the Aegean Sea, have experienced an advancing series of strong quakes during the past 80 years, beginning in 1939 when a devastating 7.8-magnitude rupture leveled the city of Erzincan and killed 33,000 people. Most recently, in 1999, 7.4-magnitude quake near the city of İzmit left 17,000 dead and half a million homeless. A few months later, another shock hit Düzce, 60 miles away. Brendan Meade, an applied computational scientist and associate professor of earth and planetary sciences, recently built a computer model of conditions in the North Anatolian Fault.