Robotic ultrasound (US) imaging has been seen as a promising solution to overcome the limitations of free-hand US examinations, i.e., interoperator variability. However, the fact that robotic US systems (RUSSs) cannot react to subject movements during scans limits their clinical acceptance. Regarding human sonographers, they often react to patient movements by repositioning the probe or even restarting the acquisition, in particular for the scans of anatomies with long structures like limb arteries. To realize this characteristic, we proposed a vision-based system to monitor the subject’s movement and automatically update the scan trajectory thus seamlessly obtaining a complete 3-D image of the target anatomy. The motion monitoring module is developed using the segmented object masks from RGB images. Once the subject is moved, the robot will stop and recompute a suitable trajectory by registering the surface point clouds of the object obtained before and after the movement using the iterative closest point (ICP) algorithm. Afterward, to ensure optimal contact conditions after repositioning US probe, a confidence-based fine-tuning process is used to avoid potential gaps between the probe and contact surface. Finally, the whole system is validated on a human-like arm phantom with an uneven surface, while the object segmentation network is also validated on volunteers. The results demonstrate that the presented system can react to object movements and reliably provide accurate 3-D images.

The amount of CO₂ released in the atmosphere has been at a continuous surge in the last decade, and in order to protect the environment from global warming, it is necessary to employ techniques like carbon capture. Developing technologies like Carbon Capture Utilization and Storage aims at mitigating the CO₂ content from the air we breathe and has garnered immense research attention. In this review, the authors have aimed to discuss the various technologies that are being used to capture the CO₂ from the atmosphere, store it and further utilize it. For utilization, researchers have developed alternatives to make profits from CO₂ by converting it into an asset. The development of newer fuel cells that consume CO₂ in exchange for electrical power to drive the industries and produce valuable hydrocarbons in the form of fuel has paved the path for more research in the field of carbon utilization. The primary focus on the article is to inspect the environmental and economic feasibility of novel technologies such as fuel cells, different electrochemical processes, and the integration of artificial intelligence and data science in them, which are designed for mitigating the percentage of CO₂ in the air.

In this study, the effect of these variables on commercial silica NP retention was presented in a fabricated flow model considering only the physical adsorption aspects of silica NP retention. From our observations, it was established that while silica NP concentration, flow rate and salt are key variables in influencing silica NP agglomeration and retention, the effect of temperature was highly subdued. The effect of salt-induced agglomeration was particularly severe at moderate salinity (≈4 wt% NaCl). To mitigate the effect of salt-induced agglomeration, a commonly used anionic surfactant, sodium dodecyl sulfate (SDS) was added to the solution and the silica NP retention was tabulated. An amount of 0.3 wt% SDS was found to negate salt-induced agglomeration significantly, paving the way for use of silica NP solutions, even in the presence of saline conditions. A section on the prospective use of artificial intelligence for this purpose has been included. This study is useful for understanding NP retention behaviour, especially in the presence of salinity and its mitigation using surfactants, in flow applications.