A Self-Adaptive Cooperative Routing Protocol for Underwater Acoustic Sensor Networks

[[abstract]]Design an effective routing protocol in underwater acoustic sensor networks (UASNs) is an important issue. Long propagation time and low DATA rate which are two major concerns for routing protocol design in UASNs will lead to the long end-to-end transmission time. This paper proposes a SelfAdaptive Cooperative Routing Protocol (SACRP) to effectively route collecting DATA to the sink in UASNs. Cooperative transmission in SACRP not only can enhance the link quality (Signalto-Noise Ratio (SNR)) to improve the network throughput but also can increase the transmission range of a node to reduce the end-to-end transmission time. Some mathematical analyses about cooperative transmission scheme are done to support SACRP protocol in the different DATA size and transmission range as well. Based on the network simulations, the proposed protocol, SACRP, has a significant performance against the related work in average end-to-end delay and packet delivery ratio.[[notice]]補正完

Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan

PurposeTo compare the diagnostic performance of radiomic analysis with machine learning (ML) model with a convolutional neural network (CNN) in differentiating thymic epithelial tumors (TETs) from other prevascular mediastinal tumors (PMTs).MethodsA retrospective study was performed in patients with PMTs and undergoing surgical resection or biopsy in National Cheng Kung University Hospital, Tainan, Taiwan, E-Da Hospital, Kaohsiung, Taiwan, and Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan between January 2010 and December 2019. Clinical data including age, sex, myasthenia gravis (MG) symptoms and pathologic diagnosis were collected. The datasets were divided into UECT (unenhanced computed tomography) and CECT (enhanced computed tomography) for analysis and modelling. Radiomics model and 3D CNN model were used to differentiate TETs from non-TET PMTs (including cyst, malignant germ cell tumor, lymphoma and teratoma). The macro F1-score and receiver operating characteristic (ROC) analysis were performed to evaluate the prediction models.ResultIn the UECT dataset, there were 297 patients with TETs and 79 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 83.95%, ROC-AUC = 0.9117) had better performance than the 3D CNN model (macro F1-score = 75.54%, ROC-AUC = 0.9015). In the CECT dataset, there were 296 patients with TETs and 77 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 85.65%, ROC-AUC = 0.9464) had better performance than the 3D CNN model (macro F1-score = 81.01%, ROC-AUC = 0.9275).ConclusionOur study revealed that the individualized prediction model integrating clinical information and radiomic features using machine learning demonstrated better predictive performance in the differentiation of TETs from other PMTs at chest CT scan than 3D CNN model

Isolation and Characterization of Novel Murine Epiphysis Derived Mesenchymal Stem Cells

BACKGROUND: While bone marrow (BM) is a rich source of mesenchymal stem cells (MSCs), previous studies have shown that MSCs derived from mouse BM (BMMSCs) were difficult to manipulate as compared to MSCs derived from other species. The objective of this study was to find an alternative murine MSCs source that could provide sufficient MSCs. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we described a novel type of MSCs that migrates directly from the mouse epiphysis in culture. Epiphysis-derived MSCs (EMSCs) could be extensively expanded in plastic adherent culture, and they had a greater ability for clonogenic formation and cell proliferation than BMMSCs. Under specific induction conditions, EMSCs demonstrated multipotency through their ability to differentiate into adipocytes, osteocytes and chondrocytes. Immunophenotypic analysis demonstrated that EMSCs were positive for CD29, CD44, CD73, CD105, CD166, Sca-1 and SSEA-4, while negative for CD11b, CD31, CD34 and CD45. Notably, EMSCs did not express major histocompatibility complex class I (MHC I) or MHC II under our culture system. EMSCs also successfully suppressed the proliferation of splenocytes triggered by concanavalin A (Con A) or allogeneic splenocytes, and decreased the expression of IL-1, IL-6 and TNF-α in Con A-stimulated splenocytes suggesting their anti-inflammatory properties. Moreover, EMSCs enhanced fracture repair, ameliorated necrosis in ischemic skin flap, and improved blood perfusion in hindlimb ischemia in the in vivo experiments. CONCLUSIONS/SIGNIFICANCES: These results indicate that EMSCs, a new type of MSCs established by our simple isolation method, are a preferable alternative for mice MSCs due to their better growth and differentiation potentialities

A Self-Powered Strain Sensor Applied to Real-Time Monitoring for Movable Structures

This study uses near-field electrospinning (NFES) technology to make a novel self-powered strain sensor and applies it to the real-time monitoring of a bending structure, so that the measurement equipment can be reduced in volume. A self-powered strain sensor consists of polyvinylidene difluoride (PVDF) fibers, a PDMS fixed substrate, and an aluminum electrode. PVDF fibers are spun with DMSO and acetone using NFES technology, with a diameter of about 8 μm, Young’s modulus of 1.1 GPa, and piezoelectric effect of up to 230 mV. The fixed substrate is a film made of PDMS by thermal curing, then adhered to the PDMS film surface of the sheet Al metal as an Al electrode, and then combined with PVDF fiber film, to become a self-powered strain sensor. As a result, the XRD β value of the self-powered strain sensor reaches 2112 and the sensitivity is increased by 20% over a traditional strain sensor. The cumulative angle algorithm can be applied to measure the angular change of the object over a unit of time or the cumulative displacement of the object over the entire period of motion. The experimental results demonstrate that the self-powered strain sensor combined with the angle accumulation algorithm may be applied to monitor the bending structure, thereby achieving continuous measurements of bending structure changes, and improving on traditional piezoelectric sensors, which can only be sensed once. In the future, self-powered strain sensors will have the ability to continuously measure in real-time, enabling the use of piezoelectric sensors for long-term monitoring of structural techniques

Design and Testing of Real-Time Sensing System Used in Predicting the Leakage of Subsea Pipeline

This study integrates the array sensing module and the flow leakage algorithm. In this study, a real-time monitoring deep-sea pipeline damage sensing system is designed to provide decision-making parameters such as damage coordinates and damage area. The array sensor module is composed of multiple YF-S201 hall sensors and controllers. YF-S201 hall sensors are arranged inside the pipeline in an array. The flow signal in the deep-sea pipeline can be transmitted to the electronic control interface to analyze the leakage position and leakage flowrate of the pipeline. The theory of this system is based on the conservation of mass. Through the flow of each sensor, it is judged whether the pipeline is damaged. When the pipeline is not damaged, the flowrate of each sensor is almost the same. When the pipeline is damaged, the flowrate will drop significantly. When the actual size of leakage in the pipeline is 5.28 cm2, the size calculated by the flowrate of hall sensors is 2.58 cm2 in average, indicating the error between experimental data and theoretical data is 46%. When the actual size of leakage in the pipeline is 1.98 cm2, the size calculated by the flowrate of hall sensors is 1.31 cm2 in average, indicating the error between experimental data and theoretical data is 21%. This can accurately confirm the location of the broken pipeline, which is between sensor A and sensor B, so that the AUV/ROV can accurately locate and perform pipeline maintenance in real time. It is expected to be able to monitor the flowrate through the array magnetic sensing module designed in this study. It can grasp the status of deep-sea pipelines, improve the quality of deep-sea extraction and pipeline maintenance speed

The learning curve for laparoscopic colectomy in colorectal cancer at a new regional hospital

Laparoscopic colorectal surgery has been extensively used, although mostly performed in medical centers or university hospitals. We analyzed the learning curve of laparoscopic colectomy in a new regional hospital and determined the experience necessary to achieve proficiency. From July 2008 to December 2013, the retrospective clinical study enrolled 240 patients who underwent laparoscopic colectomy. They were sequentially divided into Group A (Patients 1–80), Group B (Patients 81–160), and Group C (Patients 161–240). Patient demographics and perioperative parameters were analyzed. Operation time, as a measure of learning time, was analyzed using the moving-average method. All patients were comparable for age, gender, body mass index, tumor location, cancer stage, length of hospital stay, intraoperative complication, morbidity, and mortality. Group A experienced more blood loss (p < 0.01) and longer operation time (p < 0.001). All laparoscopic operation time stabilized after 85 cases. Subgroup analysis showed that operation time stabilized after 15 cases for right hemicolectomy, 15 cases for sigmoidectomy, and 22 cases for low anterior resection with total mesorectal excision. Laparoscopic colectomy for colorectal cancer in a new regional hospital is feasible and safe. It does not need additional time for learning. Laparoscopic sigmoidectomy can be considered as the initial surgery for a trainee