Sinuo Zhao

Palpation is a simple but effective method to distinguish tumors from healthy tissues. The development of miniaturized tactile sensors embedded on endoscopic or robotic devices is key to achieving precise palpation diagnosis and subsequent timely treatment. This paper reports on the fabrication and characterization of a novel tactile sensor with mechanical flexibility and optical transparency that can be easily mounted on soft surgical endoscopes and robotics. By utilizing the pneumatic sensing mechanism, the sensor offers a high sensitivity of 1.25 mbar and negligible hysteresis, enabling the detection of phantom tissues with different stiffnesses ranging from 0 to 2.5 MPa. Our configuration, combining pneumatic sensing and hydraulic actuating, also eliminates electrical wiring from the functional elements located at the robot end-effector, thereby enhancing the system safety. The optical transparency path in the sensors together with its mechanical sensing capability open interesting possibilities in the early detection of solid tumor as well as in the development of all-in-one soft surgical robots that can perform visual/mechanical feedback and optical therapy.

Palpation is a simple but effective method to differentiate tumors from healthy tissues. Development of miniaturized tactile sensors embedded on endoscopic or robotic devices are the key toward precise palpation diagnosis and the subsequent timely treatment. This paper reports on the fabrication and characterization of a novel tactile sensor with mechanical flexibility and optical transparency that can be easily mounted on soft surgical endoscopes and robotics. Utilizing the pneumatic sensing mechanism, the sensor offers a high sensitivity of 1.25 mbar and negligible hysteresis, enabling the detection of phantom tissues with different stiffnesses ranging from 0 to 2.5 MPa. Our configuration combining pneumatic sensing and hydraulic actuating also eliminates electrical wiring from the functional elements located at the robot end-effector, thereby enhancing the system safety. The optical transparency path in the sensors together with its mechanical sensing capability open interesting possibility in early detection of solid tumor as well as in the development of all in-one soft surgical robots that can perform visual/mechanical feedback and optical therapy.

Rapid urbanization nowadays demands the stable functioning of a city’s infrastructure, but the traditional infrastructure system faces numerous challenges: unpreparedness for extreme situations, aging and deterioration of existing facilities, costly maintenance, and lack of coordination. Moreover, the transformation from traditional infrastructure into smart infrastructure introduces new challenges both from the underdevelopment of information network and the limitation of AI technologies. In this paper, we evaluate, from the UV perspective, the challenges of the infrastructure system and the current status of the smart infrastructure system based on the framework of closed feedback control loops: data acquisition, communication, decision making, and action. We propose that an effective smart infrastructure system should take into consideration the interaction between the smart infrastructure system and other seven smart city subsystems: smart home, smart medicine and healthcare, intelligent transportation, urban planning and crowd management, smart energy management, smart response for city emergency, smart environmental protection, and smart humanity, and also study how the smart infrastructure system would be affected by four major impacting factors of smart cities: information flow, material cycle, lifestyle, and community. This systematic study will help us explore in-depth the complicated dynamic relationship between multiple impacting factors and propose a UV-oriented, integrated, resilient, inclusive, and sustainable development framework design to address current imminent challenges and to improve the efficiency and safety of city infrastructure through vulnerability-oriented sensing and active monitoring; defining shared responsibilities; communication reinforcements; data visualization; static and dynamic data integration; hierarchical decision making; and optimized and coordinated demand response management.

At present, rapid urbanization is bringing convenience and high efficiency to our daily lives, but the increase in GDP comes with the cost of high metabolic rates, high waste per capita, severe lack of resources, heavy pollution, and waste siege. This cost-expensive lifestyle leads to severe environmental degradation and soaring resource consumption. In this paper, we evaluate, from the UV perspective, the challenges of the environment system, including land pollution, water pollution and air pollution, and the current status of smart environmental protection systems based on the framework of closed feedback control loop: data acquisition, communication, decision-making and action. We propose that effective smart environmental protection should take into consideration the interaction between environmental protection subsystems and other seven smart city subsystems: smart home, smart medicine and healthcare, intelligent transportation, urban planning and crowd management, smart energy management, smart city infrastructure, smart response system for city emergency, and smart humanity, and also study how smart environmental protection would be affected by four major impacting factors of smart cities: information flow, material cycle, lifestyle and community. This systematic study will help us explore in depth the complicated dynamic relationship between multiple impacting factors and propose a UV-oriented, integrated, resilient, inclusive and sustainable development framework design to address current imminent challenges and to improve our living environment through real-time monitoring of pollution, crowd-sourcing based on hazard reports, identification of pollution sources, preventive waste and pollution control based on comprehensive management of material cycles and personalized lifestyle guidance and suggestions.

At present, frequent natural and human-involved disasters and urgent events pose serious threats to human society and call for the smart response system for city emergencies to improve its disaster preparedness and response. In this paper, we first investigate the challenges of the emergency response system, including the inefficiency of prediction before emergencies, uncoordinated preparedness for disasters, and lacking communication and collaboration across different departments as well as unpreparedness of secondary challenges in healthcare, environmental protection, and humanity. We then evaluate, from the UV perspective, the current status of the smart response system for city emergencies based on the framework of a closed feedback control loop: data acquisition, communication, decision-making, and action. We propose that effective smart emergency response should consider the interaction between smart response system for city emergency and other seven smart city subsystems: smart home, smart medicine and healthcare, intelligent transportation, urban planning and crowd management, smart energy management, smart city infrastructure, smart environmental protection system, and smart humanity. It should also study how smart emergency response would be affected by four major impacting factors of smart cities: information flow, material cycle, lifestyle, and community. This systematic study enables us to improve preparedness, coordination & adaptiveness, safety, robustness & resilience of the current smart emergency response system and propose a UV-oriented, integrated, resilient, inclusive, and sustainable development framework design to address current imminent challenges and improve the response-ability through 1) hierarchical emergency response procedures for individuals, communities, and cities before, during, and after emergencies 2) hybrid, integrative, needed-based data acquisition 3) effective communication channel construction and information sharing 4) adaptive decision-making based on hierarchical knowledge level 5) human-centered and capacity-focused action.