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The standard treatment for early-stage lung cancer is complete tumor excision by limited resection of the lung. Pre-operative localization is used before video-assisted thoracoscopic surgery (VATS) to improve the accuracy of pulmonary nodule excision. However, lung atelectasis and hypoxia resulting from controlling apnea during the localization procedure may affect the localization accuracy. Pre-procedural pulmonary recruitment may improve respiratory mechanics and oxygenation during localization. In this study, we investigated the potential benefits of pre-localization pulmonary recruitment prior to pulmonary ground-glass nodule localization in a hybrid operating room. We hypothesized that pre-localization pulmonary recruitment would increase localization accuracy, improve oxygenation, and prevent the need for re-inflation during the localization procedure. We retrospectively enrolled patients with multiple pulmonary nodule localizations before surgical intervention in our hybrid operating room. We compared localization accuracies between patients who had undergone pre-procedure pulmonary recruitment and patients who had not. Saturation, re-inflation rate, apnea time, procedure-related pneumothorax, and procedure time were also recorded as secondary outcomes. Patients who had undergone pre-procedure recruitment had better saturation, shorter procedure time, and higher localization accuracy. The pre-procedure pulmonary recruitment maneuver was effective in increasing regional lung ventilation, leading to improved oxygenation and localization accuracy.

The standard treatment for early-stage lung cancer is complete tumor excision by limited resection of the lung. Preoperative localization is used before video-assisted thoracoscopic surgery (VATS) to improve the accuracy of pulmonary nodule excision. However, lung atelectasis and hypoxia resulting from controlling apnea during the localization procedure may affect the localization accuracy. Pre-procedural pulmonary recruitment may improve the respiratory mechanics and oxygenation during localization. In this study, we investigated the potential benefits of pre-localization pulmonary recruitment prior to pulmonary ground-glass nodule localization in a hybrid operating room. We hypothesized that pre-localization pulmonary recruitment would increase the localization accuracy, improve oxygenation, and prevent the need for re-inflation during the localization procedure. We retrospectively enrolled patients with multiple pulmonary nodule localizations before surgical intervention in our hybrid operating room. We compared the localization accuracy between patients who had undergone pre-procedure pulmonary recruitment and patients who had not. Saturation, re-inflation rate, apnea time, procedure-related pneumothorax, and procedure time were also recorded as secondary outcomes. The patients who had undergone pre-procedure recruitment had better saturation, shorter procedure time, and higher localization accuracy. The pre-procedure pulmonary recruitment maneuver was effective in increasing regional lung ventilation, leading to improved oxygenation and localization accuracy.

The nasopharyngeal tract traps mainly coarse particles in inhaled air. Soluble carcinogenic compounds, endotoxins, and trace metals contained in these particles are potential causes of inflammation and oxidative stress which could enhance carcinogenesis. The aim of this study was to determine the association between coarse particulate matter (PM10-2.5) and nasopharyngeal cancer (NPC). A total of 521,098 men (355 cases and 520,743 non-cases), aged ≥40 years were included in this study. Data were retrieved from the Taiwan Cancer Registry, the Adult Preventive Medical Services Database, and the Air Quality Monitoring Database. PM10-2.5 was significantly associated with a higher risk of NPC after adjusting for SO2, NOx, O3, age, body mass index, smoking, alcohol drinking, betel nut chewing, exercise, hypertension, diabetes, and hyperlipidemia. With PM10-2.53 as the reference, the ORs and 95% CIs were 1.47; 1.03–2.11, 1.34; 0.94–1.91, and 1.68; 1.16–2.44 for 20.44≤PM10-2.510-2.510-2.5≥29.27 μg/m3, respectively. PM10-2.5 remained significantly associated with a higher risk of NPC after further adjustments were made for the aforementioned covariates and PM2.5. The ORs; 95% CIs were 1.42; 0.96 to 2.12, 1.41; 0.94 to 2.10, and 1.71; 1.10 to 2.66 for 20.44≤PM10-2.510-2.510-2.5≥29.27 μg/m3, respectively. In conclusion, PM10-2.5 was significantly associated with a higher risk of NPC in Taiwanese men.

This paper presents a real-time machine learning control (MLC) of articulated robotic manipulators using artificial bee colony optimization (ABC) algorithm incorporated with fuzzy theory. The modified ABC with dynamic weight is used to optimize the fuzzy structure and fractional order. The fractional parameters, fuzzy membership functions and rule base are determined by means of the ABC computation. This ABC-fuzzy hybrid learning algorithm is applied to real-time MLC of robotic manipulators by including fractional order proportional-integral-derivative (FOPID) control strategy. The MLC's control gain parameters are online tuned via the ABC-fuzzy optimization. With the kinematics analysis of a six-degree-of-freedom (DOF) articulated arm via reverse coordinates approach, an ABC-fuzzy MLC is developed to achieve motion control. A real-time operating system (RTOS) on a microprocessor collaborates with the ABC-fuzzy MLC to meet critical timing constraint by considering the dynamics of actuators. Finally, the mechatronic design and experimental setup of a six-DOF articulated robotic manipulator are constructed. Experimental results and comparative works are provided to demonstrate the merit of the proposed methods. Compared with the conventional control schemes, the proposed ABC-fuzzy MLC has theoretical and practice significance in term of real-time capability, online parameter tuning, convergent behavior and hybrid MLC. The proposed MLC methodologies are applicable to designing real-time modern controllers in both industry and academia.

This article presents a networked multirobot cyber-physical system (CPS) that employs artificial immune fuzzy optimization to achieve distributed formation control of embedded mobile robots. The artificial immune system (AIS) algorithm is combined with a fuzzy system to develop a hybrid optimization technique, called AIS-fuzzy. This metaheuristic is applied to designing the intelligent third cyber and fourth cognition levels in the networked multirobot CPS 5C structure of embedded robots to accomplish distributed formation control. The proposed CPS cyber and cognition levels with AIS-fuzzy paradigm incorporate with the first, second, and fifth levels by means of robotic sensors and actuators. System-on-a-programmable-chip (SoPC) methodology is adopted to present a pragmatic networked multirobot CPS in the field-programmable gate array chips. The presented SoPC-based AIS-fuzzy multirobot CPS features the seamless integration of computation, networking, and physical robotic processes. Experimental results are obtained, and comparative analyses are provided to illustrate the effectiveness and merits of the proposed multirobot CPS to perform distributed formation control of embedded mobile robots.

This paper presents a collision-free fuzzy formation control strategy of swarm robotic cyber-physical systems (CPSs) using a robust orthogonal firefly algorithm (OFA). The classical FA is fused with the Taguchi method and the ranking mutation process to present a hybrid artificial intelligence. This computational intelligence is employed with the fuzzy theory to develop optimal cyber and cognition levels in swarm robotic CPS 5C architecture. With robotic sensors and actuators, the OFA-fuzzy-based cyber and cognition levels are incorporated with the smart connection, data-to-information conversion, and configuration levels to design a pragmatic swarm robotic CPS using system-on-a-programmable-chip technology. The broadcast distributed control strategy and potential field are employed to address the formation control problem of swarm robotic CPSs with obstacles. In the proposed swarm robotic CPS, the embedded central processing unit, operating system, networking intellectual property (IP), and robotic custom IPs are integrated into field-programmable gate array chips. The experimental results and comparisons with other methods show the merits of the proposed swarm robotic CPS in achieving collision-free distributed formation control.