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Coin-sized Micro-needles Patch to deliver Insulin: An innovation by Shanghai University Scientists

Stem Cells Help Researchers Study The Effects of Pollution on Human Health

A Novel and Robust Muscle Activity Onset Detection Technique by Using an Unsupervised Electromyogram Learning Framework

Early Modern Humans and Neanderthals were close bedfellows

FANCD2 and REV1 Cooperate in the Protection of Nascent DNA Strands in Response to Replication Stress

Spiders' Foraging Strategies Have Cascading Effects on Litter Decomposition Rates

Yingli Announces Sale of 18.8 MW Solar Power Plant in the UK to NextEnergy Solar Fund

China's Award Winning Desertification Control in Kubiqi Desert

Earthquake Early Warning System for Nepal

Mechanical Coupling Mechanism of a Mechanical Force-sensing Channel Protein Discovered by IBP Scientists

Jiahui International Hospital and Brigham and Women's Hospital to Co-Develop Women's Health Center of Excellence in Shanghai

Big Grain1: The ‘Mr. BIG’ for Crops to Grow Bigger by Regulating Auxin Transport in Rice

When there is no Queen in the house, Asian Hive Bees Avoid Risky Foraging for Reproduction

UNICEF HK joins hands with Government to help Mothers sustain Breastfeeding

Fresenius Medical Care Springs into Action after Deadly Tianjin Explosion

In recent years, the prevalence of cardiovascular diseases has been growing geometrically, especially among the elderly. Real-time heart rate monitoring becomes an effective measure for preventing cardiovascular diseases. Traditional contact-based real-time heart rate measurement methods require long-term wearing of contact electrodes, which can cause inconvenience to the elderly population. In this study, a wide spectrum full color night vision elderly face video heart rate automatic measurement and early warning system was designed and developed by combining imaging photoplethysmography (IPPG), video acquisition equipment based on starlight wide spectrum camera and high-speed face recognition signal processing algorithm based on deep learning. By constructing a software and hardware platform, a comprehensive evaluation of the system’s application in elderly facial video heart rate measurement experiments was conducted.

Warning systems have been proposed to reduce driver cognitive and judgment load as driver systems for traffic safety. The system's efficacy could be decreased if the driver feels annoyance and/or mistrust with inappropriate warning timing, etc. This paper explores the possibility of personalized warning timing to cope with this problem. First, we propose a new warning method for rear-side obstacles using a driver's perceptual risk model that we derived from the analysis of the driver's deceleration behavior. Validity of the warning method will be shown by driving simulator experiments. In addition, there exists individual difference in expectation of meaning of warning. Thus, differences of the efficacy and driver's response behaviors against warning will be analyzed based on the difference of their expectation.

Tsunami waves are considered the most dangerous natural hazard affecting the population of the world living near the coastal belts. With the increasing intensity of economic exploitation of coasts there is also an increase in socio-economic consequences resulting from the hazardous action of tsunami waves generated from submarine seismic activity and other causes. On 26 December 2004, the countries within the vicinity of East Indian Ocean experienced the most devastating tsunami in recorded history. This tsunami was triggered by an earthquake of magnitude 9.0 on the Richter scale at 3.4°N, 95.7°E off the coast of Sumatra in the Indonesian Archipelago at 06:29 hrs IST (00:59 hrs GMT).

As of now (September, 2005), the only Tsunami Warning System (TWS) that is in existence is the one for the Pacific Ocean, which began in the late 1940s. Following the recent disastrous tsunami of 26 December 2004 in the Indian Ocean, the nations around the Indian Ocean rim are now working together to establish a tsunami warning system which should become operational in the near future. One of the most basic information that an Indian Ocean tsunami warning center should have at its disposal, is information on tsunami travel times to various coastal locations surrounding the Indian Ocean rim, as well to several island locations. Devoid of this information, no ETA's (expected times of arrival) can be included in the real-time tsunami warnings.

The importance of ETA for tsunami warning system motivated the computation of arrival times comprising 250 representative coastal locations from 35 countries, showing the feasibility of developing a TWS in a relatively short time-span. Numerical accuracy in computating arrival times for this energetic event has been verified from in situ tide gauge data and satellite track data from Jason-I and Topex/Poseidon in the Indian Ocean and also from coastal stations off South Africa. The expected outcome of this work is to develop a widely distributed tsunami travel time (TTT) atlas which can serve as a valuable information database to reduce warning time in the event of tsunamis in the Indian Ocean and promote awareness among the population dwelling in the littoral belts of the South-Asian countries.

On 26th December 2004, the countries within the vicinity of East Indian Ocean experienced the most devastating tsunami in recorded history. This tsunami was triggered by an earthquake of magnitude 9.0 on the Richter scale at 3.4°N, 95.7°E off the coast of Sumatra in the Indonesian Archipelago at 06:29 hrs IST (00:59 hrs GMT). One of the most basic information that any tsunami warning center should have at its disposal, is information on Tsunami Travel Times (TTT) to various coastal locations surrounding the Indian Ocean rim, as well as to several island locations. Devoid of this information, no ETA's (expected times of arrival) can be included in the real-time tsunami warnings. The work describes on development of a comprehensive TTT atlas providing ETA's to various coastal destinations in the Indian Ocean rim. This Atlas was first released on the first anniversary of the Indian Ocean Tsunami and was dedicated to the victims. Application of soft computing tools like Artificial Neural Network (ANN) for prediction of ETA can be immensely useful in a real-time mode. The major advantage of using ANN in a real-time tsunami travel time prediction is its high merit in producing ETA at a much faster time and also simultaneously preserving the consistency of prediction. Overall, it can be mentioned that modern technology can prevent or help in minimizing the loss of life and property provided we integrate all essential components in the warning system and put it to the best possible use.

An intelligent wireless sensor network system called "RockNet" was developed and tested for rock fall surveillance. It provides real-time detection with warning capabilities for many lifelines such as: express highways, railways, roads as well as hiking trails, ski slopes, mountain climbing routes and for construction workers or objects that should be temporarily protected in case of imminent danger in rock fall zones. RockNet consists of an array of sensor nodes easily distributed in the down slope zone of an area prone to rockfalls. The RockNet system monitors the vibration signals caused by the impacts of falling rocks or boulders. It is capable of analyzing the received signals, compare them with several criteria and automatically decide on sending an alarm to agencies, stop trains or control traffic lights to avoid accidents. RockNet is a cost-effective, easy to use and suitable geohazard prediction tool to reduce the risk due to rockfalls in vulnerable areas. A prototype system has successfully been tested in the Swiss mountains for several months.