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Abstract Background Tunneling nanotubes (TNTs) are special membrane structures for intercellular communications. Vital cargoes (such as mitochondria) could be delivered from healthy cells to rescue damaged ones through TNTs. The TNTs could be utilized for the purpose of systematic delivery of therapeutic agents between cells. However, there are insufficient studies on the controlled enhancement of TNT formations. The purpose of this study is to understand how macrophages influence the TNT formation in cancer cells. Results Here we compared the capabilities of inducing TNTs in human pancreatic cancer cells (PANC-1) of the media conditioned by M0, M1 and M2 macrophages derived from THP-1 cells. The M0 and M1 macrophage conditioned media promoted TNT formation. Using a focused ion beam to cut through a TNT, we observed tunnel-like structures inside dense cytoskeletons with scanning electron microscopy. The TNT formation correlated with raised motility, invasion, and epithelial–mesenchymal transition in the PANC-1 cells. Mitochondria and lysosomes were also found to be transported in the TNTs. Conclusions These results suggest that TNT formation could be one of the responses to the immune stress in pancreatic cancer cells caused by M0 and M1 macrophages. This finding is valuable for the development of macrophage-targeting cancer therapy.

From traditionally handmade items to the ability of people to use machines to process and even to human-robot collaboration, there are many risks. Traditional manual lathes and milling machines, sophisticated robotic arms, and computer numerical control (CNC) operations are quite dangerous. To ensure the safety of workers in automated factories, a novel and efficient warning-range algorithm is proposed to determine whether a person is in the warning range, introducing YOLOv4 tiny-object detection algorithms to improve the accuracy of determining objects. The results are displayed on a stack light and sent through an M-JPEG streaming server so that the detected image can be displayed through the browser. According to the experimental results of this system installed on a robotic arm workstation, it is proved that it can ensure recognition reaches 97%. When a person enters the dangerous range of the working robotic arm, the arm can be stopped within about 50 ms, which will effectively improve the safety of its use.

Anomalies are a set of samples that do not follow the normal behavior of the majority of data. In an industrial dataset, anomalies appear in a very small number of samples. Currently, deep learning-based models have achieved important advances in image anomaly detection. However, with general models, real-world application data consisting of non-ideal images, also known as poison images, become a challenge. When the work environment is not conducive to consistently acquiring a good or ideal sample, an additional adaptive learning model is needed. In this work, we design a potential methodology to tackle poison or non-ideal images that commonly appear in industrial production lines by enhancing the existing training data. We propose Hierarchical Image Transformation and Multi-level Features (HIT-MiLF) modules for an anomaly detection network to adapt to perturbances from novelties in testing images. This approach provides a hierarchical process for image transformation during pre-processing and explores the most efficient layer of extracted features from a CNN backbone. The model generates new transformations of training samples that simulate the non-ideal condition and learn the normality in high-dimensional features before applying a Gaussian mixture model to detect the anomalies from new data that it has never seen before. Our experimental results show that hierarchical transformation and multi-level feature exploration improve the baseline performance on industrial metal datasets.

More engaging and accessible solutions that offer outstanding user experience are needed. The virtual desktop infrastructure (VDI) is the first user access part of the cloud. Therefore, the performance of the VDI has a significant influence on the network. The graphics processing unit (GPU) can accelerate performance by assigning each virtual machine a dedicated GPU for performance-boosting to improve the user experience. In this paper, we propose a high-performance VDI using an integration of a GPU on OpenStack with a PCI pass-through. It might have certain benefits over using virtualized hardware, such as lower latency, better performance, and more functionality. The evaluation and comparison of the computer processing unit (CPU) and GPU are presented among the different benchmark tools.

In a traditional multistate quickest path problem (MQPP), the system reliability is evaluated based on a strict assumption that the net flow into and out of a system is equal to zero. However, certain networks, which are known as deteriorated networks, suffer a loss due to the deterioration effect, resulting a delivery shortage. For example, the data or goods will deteriorate or decay because the transmission distance is too long, which affects whether the delivered data or goods arrive intact. To provide a practical solution to this problem, a novel MQPP model, known as the deteriorated MQPP (MQPPde) model, is proposed in this work. The goal is to evaluate the system reliability, which is defined as the probability that the end user receives at least d units of data or goods in transmission time T in the case of a MQPP with the deterioration effect. A simple path-based algorithm based on an integer programming model of the flow conservation law is presented to generate all of the lower boundary points (d, T)-MPdes. Next, the reliability of the MQPPde model can be calculated in terms of all of the (d, T)-MPdes.

In order to reduce carbon dioxide emissions, pressure swing adsorption (PSA) process was studied to capture carbon dioxide from flue gas in a coal-fired power plant. Pressure swing adsorption features its low energy consumption, low investment, and simple operation. This study aims to capture carbon dioxide from flue gas by PSA process for at least 85 % CO2 purity and with the other stream of more than 90 % N2 purity. To validate the accuracy of the PSA simulation program, the extended Langmuir-Freundlich equation was adopted to fit measured equilibrium data to describe the adsorption equilibrium of adsorbent zeolite 13X. Next, the simulation study used the linear driving force model and compared the results of breakthrough curves and desorption curves between experiments and simulation to verify the accuracy of the mass transfer coefficient kLDF value in linear driving force model. The agreement between experimental data and the simulation results is good. Further, the simulation was verified with the 100-hour cyclic-steady-state experiment of the 3-bed 9-step PSA process studied. Flue gas after desulphurisation and water removal (13.5 % CO2, 86.5 % N2) of subcritical 1 kW coal-fired power plant was taken as feed to the designed 3-bed 9-step PSA process. To find the optimal operating conditions, the central composite design (CCD) was used. After analysis, optimal operating conditions were obtained to produce a bottom product at 89.20 % CO2 purity with 88.20 % recovery, and a top product at 98.49 % N2 purity with 93.56 % recovery. The mechanical energy consumption was estimated to be 1.17 – 1.41 GJ/t-CO2.

In the global wave of automation, logistics and manufacturing are indispensable and important industries. Among them, the related automatic warehousing system is even more urgently needed. There are quite a few cases of using robotic arms in the current industry cargo stacking operations. Traditional operations require engineers to plan the stacking path for the robotic arm. If the size of the object changes, it will take extra time to re-plan the work path. Therefore, in recent years, quite a lot of automatic palletizing software has been developed; however, none of it has a detection mechanism for stacking correctness and personnel safety. As a result, in this research, an intelligent robotic palletizer system is developed based on a self-developed symmetrical algorithm to stack the goods in a staggered arrangement to ensure the overall structure. Innovatively, it is proposed to check the arrangement status and warnings during the visual stack inspection to ensure the correctness of the stacking process. Besides, an AI algorithm is imported to ensure that personnel cannot enter the set dangerous area during the work of the robotic arm to improve safety during stacking. In addition to uploading the relevant data to the cloud database in real time, the stacking process combined database and vision system also provide users with real-time monitoring of system information.

The pressure swing adsorption (PSA) process was used to capture carbon dioxide (CO2) from the flue gas of a coal-fired power plant to reduce CO2 emissions. Herein, CO2 was captured from flue gas using the PSA process for at least 85 vol% CO2 purity and with the other exit stream from the process of more than 90 vol% N2 purity. The extended Langmuir–Freundlich isotherm was used for calculating the equilibrium adsorption capacity, and the linear driving force model was used to describe the gas adsorption kinetics. We compared the results of breakthrough curves obtained through experiments and simulations to verify the accuracy of the mass transfer coefficient. The flue gas obtained after desulphurization and water removal (13.5 vol% CO2 and 86.5 vol% N2) from a subcritical 1-kW coal-fired power plant served as the feed for the designed three-bed, nine-step PSA process. To determine optimal operating conditions for the process, the central composite design (CCD) was used. After CCD analysis, optimal operating conditions with a feed pressure of 3.66 atm and a vacuum pressure of 0.05 atm were obtained to produce a bottom product with a CO2 purity of 89.20 vol% and a recovery of 88.20%, and a top product with a N2 purity of 98.49 vol% and a recovery of 93.56%. The mechanical energy consumption was estimated to be 1.17 GJ/t-CO2.

The nature and characteristics of free-burning wildland fires have significant economic, safety, and environmental impacts. Additionally, the increase in global warming has led to an increase in the number and severity of wildfires. Hence, there is an increasing need for accurately calculating the probability of wildfire propagation in certain areas. In this study, we firstly demonstrate that the landscapes of wildfire propagation can be represented as a scale-free network, where the wildfire is modeled as the scale-free network whose degree follows the power law. By establishing the state-related concepts and modifying the Binary-Addition-Tree (BAT) together with the PageRank, we propose a new methodology to serve as a reliable tool in predicting the probability of wildfire propagation in certain areas. Furthermore, we demonstrate that the proposed maximum-state PageRank used in the methodology can be implemented separately as a fast, simple, and effective tool in identifying the areas that require immediate protection. The proposed methodology and maximum-state PageRank are validated in the example generated from the Barabási-Albert model in the study.