Nanobiosensors for Biomedical, Environmental, and Food Monitoring Applications

2021 ◽  
pp. 131540
Author(s):  
Pradeep S. Thakur ◽  
Muniappan Sankar
Langmuir ◽  
2021 ◽  
Vol 37 (11) ◽  
pp. 3508-3520
Author(s):  
Poushali Das ◽  
Sayan Ganguly ◽  
Shlomo Margel ◽  
Aharon Gedanken

Author(s):  
Cecilia Klauber ◽  
Komal S. Shetye ◽  
Zeyu Mao ◽  
Thomas J. Overbye ◽  
Jennifer Gannon ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1936
Author(s):  
Tsun-Kuang Chi ◽  
Hsiao-Chi Chen ◽  
Shih-Lun Chen ◽  
Patricia Angela R. Abu

In this paper, a novel self-optimizing water level monitoring methodology is proposed for smart city applications. Considering system maintenance, the efficiency of power consumption and accuracy will be important for Internet of Things (IoT) devices and systems. A multi-step measurement mechanism and power self-charging process are proposed in this study for improving the efficiency of a device for water level monitoring applications. The proposed methodology improved accuracy by 0.16–0.39% by moving the sensor to estimate the distance relative to different locations. Additional power is generated by executing a multi-step measurement while the power self-optimizing process used dynamically adjusts the settings to balance the current of charging and discharging. The battery level can efficiently go over 50% in a stable charging simulation. These methodologies were successfully implemented using an embedded control device, an ultrasonic sensor module, a LORA transmission module, and a stepper motor. According to the experimental results, the proposed multi-step methodology has the benefits of high accuracy and efficient power consumption for water level monitoring applications.


2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
T Oikonomidi ◽  
P Ravaud ◽  
A James ◽  
E Cosson ◽  
V Montori ◽  
...  

Abstract Background Remote digital monitoring (RDM, i.e., using digital devices to monitor patients' health and behavior) is a novel care model that can improve health outcomes for people with chronic conditions. However, it could be intrusive to patients' lives. We sought to understand which aspects of RDM make it intrusive to patients and why. Methods We performed content analysis of qualitative data collected by using open-ended questions in an international, online survey with a convenience sample of adults with type 1 or 2 diabetes (February-July 2019). Participants were first shown scenarios describing possible RDM features (i.e. different RDM tools [for glucose or food monitoring], feedback loops [receiving feedback in consultation, or remotely by a physician, or by artificial intelligence], and data handling options [by the public or private sector]). Results We analyzed data from 709 participants from 24 countries (38% men, median age 38, 54% type 1). Participants found RDM burdensome (n = 468). Burden arose from RDM features that caused disruption in daily life (e.g., alerts), features that may invite undesirable attention in public (e.g., visible wearable sensors may invite questions about one's health), or from having to adapt one's life to fit in RDM (e.g., adapt one's mealtime routine around food monitoring). Participants wanted control, particularly over sharing food-monitoring data with health care professionals in real-time to receive feedback (n = 440). They felt RDM could expose a delicate topic to 'surveillance' by authority figures (i.e., their data may 'reveal' poor dietary habits, leading to criticism by physicians). Intrusion could take the form of RDM eroding the patient-physician relationship (n = 34), or fear of data misuse (n = 206), which was associated with private-sector financial interests. Conclusions Our findings offer directions for minimally intrusive RDM design and show that digital health may cause concerns about stigma and treatment burden. Key messages Remote digital monitoring is intrusive when it increases treatment burden and limits patients’ control over their own health. “Minimally intrusive” digital health design could increase patient acceptability and, ultimately, foster scalability.


Nano Energy ◽  
2021 ◽  
pp. 106140
Author(s):  
Guosheng Hu ◽  
Zhiran Yi ◽  
Lijun Lu ◽  
Yang Huang ◽  
Yueqi Zhai ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document