Inspired by the natural spider web structure, this study innovatively designed an omnidirectional strain sensor array with a bioinspired spider web configuration. Using Ti₃C₂T_x (MXene) conductive ink and 3D printing technology, the sensor array was successfully fabricated. The strain sensor array leverages the isotropic strain response characteristics of the spider web structure, combined with a multi-class multi-output neural network, to achieve signal decoupling of the sensor array, enabling accurate identification and differentiation of both strain direction and magnitude. Within the 0-10% strain range, the sensor demonstrated a gauge factor (GF) of 26.3, with an identification accuracy of approximately 97% for strain magnitude and direction under various surface stimuli. This research provides a novel approach for achieving both high sensitivity and reliability in strain detection, demonstrating potential applications in human motion monitoring and multi-directional stress sensing. Furthermore, it offers promising prospects for applications in intelligent robotics and wearable health monitoring devices.

Researchers present a data‑driven media monitoring system that turns newspaper content into early‑warning risk signals for anti‑money laundering (AML) supervision. Using entity- and theme-based keywords scored with BM25 and aggregated into weekly indicators, the system flags periods requiring analyst attention. Event analysis of eight ICIJ offshore leaks (2013–2021) shows timely detection for Belgian banks; robustness checks assess machine translation and a prompt‑based alternative.

Current clinical protocols predominantly utilize mononuclear gadolinium(III) complexes as contrast agents. Recently, coordination clusters composed of multi-nuclear paramagnetic metal ions have demonstrated promisingly higher relaxation rates as MRI contrast agents and adaptable stability in various solutions, offering promising medical application prospects. This review mainly highlights such advancements, focusing on the influence of ligand selection and structural design on the relaxation rates of metal clusters as MRI contrast agents.

As electric vehicles surge onto our roads and portable devices dominate our daily lives, lithium-ion batteries have become the invisible powerhouses of modern technology. Yet beneath their sleek exteriors lies a critical vulnerability: manufacturing defects that can transform these energy storage marvels into potential safety hazards. A recently published review sheds light on this pressing issue, revealing how tiny metal particles, especially copper contaminants, can trigger catastrophic battery failures.

Picture this: a self-driving car smoothly navigating treacherous mountain roads with consecutive hairpin turns – a scenario that would challenge even the most experienced human drivers. This vision is now closer to reality, thanks to groundbreaking research that harnesses the power of artificial intelligence to tackle one of autonomous driving's most formidable challenges.

In a pioneering study that explores the intricate relationship between industrial output, coal consumption, and life expectancy, researchers are shedding light on the environmental and health dynamics in ASEAN nations. The study, titled "Coal Consumption as a Moderator in the Link Between Industrial Output and Life Expectancy in ASEAN Nations," is led by Prof. Abdul Rahim Ridzuan from the Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) at Universiti Teknologi MARA in Shah Alam, Selangor, Malaysia, and the Centre for Economic Development and Policy (CEDP) at Universiti Malaysia Sabah in Kota Kinabalu, Sabah, Malaysia. This research offers valuable insights into how coal consumption influences both economic growth and public health in the region.