ABSTRACT
The outbreak of coronavirus disease (COVID-19) has swept across more than 180 countries and territories since late January 2020. As a worldwide emergency response, governments have implemented various measures and policies, such as self-quarantine, travel restrictions, work from home, and regional lockdown, to control the spread of the epidemic. These countermeasures seek to restrict human mobility because COVID-19 is a highly contagious disease that is spread by human-to-human transmission. Medical experts and policymakers have expressed the urgency to effectively evaluate the outcome of human restriction policies with the aid of big data and information technology. Thus, based on big human mobility data and city POI data, an interactive visual analytics system called Epidemic Mobility (EpiMob) was designed in this study. The system interactively simulates the changes in human mobility and infection status in response to the implementation of a certain restriction policy or a combination of policies (e.g., regional lockdown, telecommuting, screening). Users can conveniently designate the spatial and temporal ranges for different mobility restriction policies. Then, the results reflecting the infection situation under different policies are dynamically displayed and can be flexibly compared and analyzed in depth. Multiple case studies consisting of interviews with domain experts were conducted in the largest metropolitan area of Japan (i.e., Greater Tokyo Area) to demonstrate that the system can provide insight into the effects of different human mobility restriction policies for epidemic control, through measurements and comparisons.
Subject(s)
COVID-19 , Humans , Communicable Disease Control , Computer Graphics , Quarantine/methods , TravelABSTRACT
BACKGROUND: Suspension training (SET) is a method of neuromuscular training that enables the body to carry out active training under unstable support through a suspension therapy system. However, there have been few reports in the literature on the application of SET to anterior cruciate ligament reconstruction (ACLR) patients. It is not clear what aspects of the patient's function are improved after SET. AIM: To investigate the effect of SET on the neuromuscular function, postural control, and knee kinematics of patients after ACLR surgery. METHODS: Forty participants were randomized to an SET group or a control group. The SET group subjects participated in a SET protocol over 6 wk. The control group subjects participated in a traditional training protocol over 6 wk. Isokinetic muscle strength of the quadriceps and hamstrings, static and dynamic posture stability test, and relative translation of the injured knee were assessed before and after training. RESULTS: The relative peak torque of the quadriceps and hamstrings in both groups increased significantly (P < 0.001), and the SET group increased by a higher percentage than those in the control group (quadriceps: P = 0.004; hamstrings: P = 0.011). After training, both groups showed significant improvements in static and dynamic posture stability (P < 0.01), and the SET group had a greater change than the control group (P < 0.05). No significant improvement on the relative translation of the injured knee was observed after training in either group (P > 0.05). CONCLUSION: Our findings show that SET promotes great responses in quadriceps and hamstring muscle strength and balance function in ACLR patients.
ABSTRACT
We introduce an energy-efficient Rate Adaptive Media Access Control (RA-MAC) algorithm for long-lived Wireless Sensor Networks (WSNs). Previous research shows that the dynamic and lossy nature of wireless communications is one of the major challenges to reliable data delivery in WSNs. RA-MAC achieves high link reliability in such situations by dynamically trading off data rate for channel gain. The extra gain that can be achieved reduces the packet loss rate which contributes to reduced energy expenditure through a reduced numbers of retransmissions. We achieve this at the expense of raw bit rate which generally far exceeds the application's link requirement. To minimize communication energy consumption, RA-MAC selects the optimal data rate based on the estimated link quality at each data rate and an analytical model of the energy consumption. Our model shows how the selected data rate depends on different channel conditions in order to minimize energy consumption. We have implemented RA-MAC in TinyOS for an off-the-shelf sensor platform (the TinyNode) on top of a state-of-the-art WSN Media Access Control Protocol, SCP-MAC, and evaluated its performance by comparing our implementation with the original SCP-MAC using both simulation and experiment.
