The Service Quality Evaluation of Mobile Communication from Quality Improvement Perspective ----a case study on China telecom in Wuchang District Wuhan City

Based on SERVAUAL model, this paper brings in the entropy method to rank quality improvement (QI) priority for service attributes, and a service quality evaluation(SQE) model integrating competitive analyses has been structured to evaluate the mobile communication service quality (SQ) for Wuhan Branch of China Telecom(WBCT). The research shows that the QI priority of 22 service attributes has changed as adopts entropy method comparing with gap-based SERVQUAL. The service attributes that finally should be improved have changed from Q20(Various business charges reasonable) and Q22(Record customer complaints and improve) to Q21(provide customers all kinds of value-added services) and Q11(Staff serves with high efficiency)

Curvilinear object segmentation in medical images based on ODoS filter and deep learning network

Automatic segmentation of curvilinear objects in medical images plays an important role in the diagnosis and evaluation of human diseases, yet it is a challenging uncertainty in the complex segmentation tasks due to different issues such as various image appearances, low contrast between curvilinear objects and their surrounding backgrounds, thin and uneven curvilinear structures, and improper background illumination conditions. To overcome these challenges, we present a unique curvilinear structure segmentation framework based on an oriented derivative of stick (ODoS) filter and a deep learning network for curvilinear object segmentation in medical images. Currently, a large number of deep learning models emphasize developing deep architectures and ignore capturing the structural features of curvilinear objects, which may lead to unsatisfactory results. Consequently, a new approach that incorporates an ODoS filter as part of a deep learning network is presented to improve the spatial attention of curvilinear objects. Specifically, the input image is transfered into four-channel image constructed by the ODoS filter. In which, the original image is considered the principal part to describe various image appearance and complex background illumination conditions, a multi-step strategy is used to enhance the contrast between curvilinear objects and their surrounding backgrounds, and a vector field is applied to discriminate thin and uneven curvilinear structures. Subsequently, a deep learning framework is employed to extract various structural features for curvilinear object segmentation in medical images. The performance of the computational model is validated in experiments conducted on the publicly available DRIVE, STARE and CHASEDB1 datasets. The experimental results indicate that the presented model yields surprising results compared with those of some state-of-the-art methods.Comment: 20 pages, 8 figure

RORα Suppresses Cancer-Associated Inflammation by Repressing Respiratory Complex I-Dependent ROS Generation

Breast cancer development is associated with macrophage infiltration and differentiation in the tumor microenvironment. Our previous study highlights the crucial function of reactive oxygen species (ROS) in enhancing macrophage infiltration during the disruption of mammary tissue polarity. However, the regulation of ROS and ROS-associated macrophage infiltration in breast cancer has not been fully determined. Previous studies identified retinoid orphan nuclear receptor alpha (RORα) as a potential tumor suppressor in human breast cancer. In the present study, we showed that retinoid orphan nuclear receptor alpha (RORα) significantly decreased ROS levels and inhibited ROS-mediated cytokine expression in breast cancer cells. RORα expression in mammary epithelial cells inhibited macrophage infiltration by repressing ROS generation in the co-culture assay. Using gene co-expression and chromatin immunoprecipitation (ChIP) analyses, we identified complex I subunits NDUFS6 and NDUFA11 as RORα targets that mediated its function in suppressing superoxide generation in mitochondria. Notably, the expression of RORα in 4T1 cells significantly inhibited cancer metastasis, reduced macrophage accumulation, and enhanced M1-like macrophage differentiation in tumor tissue. In addition, reduced RORα expression in breast cancer tissue was associated with an increased incidence of cancer metastasis. These results provide additional insights into cancer-associated inflammation, and identify RORα as a potential target to suppress ROS-induced mammary tumor progression

An Emergency-Adaptive Routing Scheme for Wireless Sensor Networks for Building Fire Hazard Monitoring

Fire hazard monitoring and evacuation for building environments is a novel application area for the deployment of wireless sensor networks. In this context, adaptive routing is essential in order to ensure safe and timely data delivery in building evacuation and fire fighting resource applications. Existing routing mechanisms for wireless sensor networks are not well suited for building fires, especially as they do not consider critical and dynamic network scenarios. In this paper, an emergency-adaptive, real-time and robust routing protocol is presented for emergency situations such as building fire hazard applications. The protocol adapts to handle dynamic emergency scenarios and works well with the routing hole problem. Theoretical analysis and simulation results indicate that our protocol provides a real-time routing mechanism that is well suited for dynamic emergency scenarios in building fires when compared with other related work

Cross-layer Routing in Wireless Sensor Networks for Machine-to-Machine Intelligent Hazard Monitoring Applications

Abstract-Machine-to-Machine (M2M) technologies allow network-to-device communications. M2M covers a wide scope of technologies including sensing and wireless networking protocols. Hazard monitoring applications based M2M such as monitoring using wireless sensor networks (WSNs) are challenged by realtime and interference-aware requirements. The designed communication mechanisms need to guarantee efficient M2M communication and performance management. In hazard monitoring applications, network topology changes rapidly due to device failures. Cross-layer design is an effective scheme to improve communication performance. In this paper, we propose a novel cross-layer mechanism with joint power control, dynamic link scheduling and routing (JPDSR) in hazard scenarios. The joint mechanism of routing, power control and link scheduling with double frame scheme guarantees a high probability of interference-aware and real-time data delivery in hazard according to event priorities. We conduct simulations and compare it with related work. The simulation results show that our routing has better performance that is more suitable for hazard monitoring applications

Label-free microfluidic paper-based electrochemical aptasensor for ultrasensitive and simultaneous multiplexed detection of cancer biomarkers

Simultaneous detection of multiple tumor biomarkers in body fluids could facilitate early diagnosis of lung cancer, so as to provide scientific reference for clinical treatment. This paper depicted a multi-parameter paper-based electrochemical aptasensor for simultaneous detection of carcinoembryonic antigen (CEA) and neuron-specific enolase (NSE) in a clinical sample with high sensitivity and specificity. The paper-based device was fabricated through wax printing and screen-printing, which enabled functions of sample filtration and sample auto injection. Amino functional graphene (NG)-Thionin (THI)- gold nanoparticles (AuNPs) and Prussian blue (PB)- poly (3,4- ethylenedioxythiophene) (PEDOT)- AuNPs nanocomposites were synthesized respectively. They were used to modify the working electrodes not only for promoting the electron transfer rate, but also for immobilization of the CEA and NSE aptamers. A label-free electrochemical method was adopted, enabling a rapid simple point-of-care testing. Experimental results showed that the proposed multi-parameter aptasensor exhibited good linearity in ranges of 0.01-500 ng mL for CEA (R  = 0.989) and 0.05-500 ng mL for NSE (R  = 0.944), respectively. The limit of detection (LOD) was 2 pg mL for CEA and 10 pg mL for NSE. In addition, the device was evaluated using clinical serum samples and received a good correlation with large electrochemical luminescence (ECL) equipment, which would offer a new platform for early cancer diagnostics, especially in those resource-limit areas

An Emergency-Adaptive Routing Scheme for Wireless Sensor Networks for Building Fire Hazard Monitoring

Fire hazard monitoring and evacuation for building environments is a novel application area for the deployment of wireless sensor networks. In this context, adaptive routing is essential in order to ensure safe and timely data delivery in building evacuation and fire fighting resource applications. Existing routing mechanisms for wireless sensor networks are not well suited for building fires, especially as they do not consider critical and dynamic network scenarios. In this paper, an emergency-adaptive, real-time and robust routing protocol is presented for emergency situations such as building fire hazard applications. The protocol adapts to handle dynamic emergency scenarios and works well with the routing hole problem. Theoretical analysis and simulation results indicate that our protocol provides a real-time routing mechanism that is well suited for dynamic emergency scenarios in building fires when compared with other related work

The Transitions Between Dynamic Micro-States Reveal Age-Related Functional Network Reorganization

Normal dynamic change in human brain occurs with age increasing, yet much remains unknown regarding how brain develops, matures, and ages. Functional connectivity analysis of the resting-state brain is a powerful method for revealing the intrinsic features of functional networks, and micro-states, which are the intrinsic patterns of functional connectivity in dynamic network courses, and are suggested to be more informative of brain functional changes. The aim of this study is to explore the age-related changes in these micro-states of dynamic functional network. Three healthy groups were included: the young (ages 21–32 years), the adult (age 41–54 years), and the old (age 60–86 years). Sliding window correlation method was used to construct the dynamic connectivity networks, and then the micro-states were individually identified with clustering analysis. The distribution of age-related connectivity variations in several intrinsic networks for each micro-state was analyzed then. The micro-states showed substantial age-related changes in the transitions between states but not in the dwelling time. Also there was no age-related reorganization observed within any micro-state. But there were reorganizations observed in the transition between them. These results suggested that the identified micro-states represented certain underlying connectivity patterns in functional brain system, which are similar to the intrinsic cognitive networks or resources. In addition, the dynamic transitions between these states were probable mechanisms of reorganization or compensation in functional brain networks with age increasing