Understanding User’s Switching Intention on Mobile Payment Platforms

With the development of mobile payment (m-payment) service, the competition accordingly increases among m-payment market. Users face multiple choices when adopting m-payment services. It is critical for both scholars and m-payments providers to understand what the underlying factors can influence user’s switching from one incumbent m-payment platform to another. To solve this question, we employ a push-pull-mooring (PPM) framework to build the research model. We propose that user’s dissatisfaction on incumbent m-payment provider is the main push factor for user’s switching. The attractiveness of alternative and peer influence are the pull factors influencing user’s switching. Cognitive lock-in, as the mooring factor, could influence switching intention directly. Additionally, we posit that cognitive lock-in can moderate the effects of both push and pull factors on user’s switching intention. This study will use survey methodology and structural equation modelling approach to test the hypotheses

Predicting structure and stability for RNA complexes with intermolecular loop–loop base-pairing

RNA loop–loop interactions are essential for genomic RNA dimerization and regulation of gene expression. In this article, a statistical mechanics-based computational method that predicts the structures and thermodynamic stabilities of RNA complexes with loop–loop kissing interactions is described. The method accounts for the entropy changes for the formation of loop–loop interactions, which is a notable advancement that other computational models have neglected. Benchmark tests with several experimentally validated systems show that the inclusion of the entropy parameters can indeed improve predictions for RNA complexes. Furthermore, the method can predict not only the native structures of RNA/RNA complexes but also alternative metastable structures. For instance, the model predicts that the SL1 domain of HIV-1 RNA can form two different dimer structures with similar stabilities. The prediction is consistent with experimental observation. In addition, the model predicts two different binding sites for hTR dimerization: One binding site has been experimentally proposed, and the other structure, which has a higher stability, is structurally feasible and needs further experimental validation

Simultaneous Multiple Object Detection and Pose Estimation using 3D Model Infusion with Monocular Vision

Multiple object detection and pose estimation are vital computer vision tasks. The latter relates to the former as a downstream problem in applications such as robotics and autonomous driving. However, due to the high complexity of both tasks, existing methods generally treat them independently, which is sub-optimal. We propose simultaneous neural modeling of both using monocular vision and 3D model infusion. Our Simultaneous Multiple Object detection and Pose Estimation network (SMOPE-Net) is an end-to-end trainable multitasking network with a composite loss that also provides the advantages of anchor-free detections for efficient downstream pose estimation. To enable the annotation of training data for our learning objective, we develop a Twin-Space object labeling method and demonstrate its correctness analytically and empirically. Using the labeling method, we provide the KITTI-6DoF dataset with $\sim7.5$K annotated frames. Extensive experiments on KITTI-6DoF and the popular LineMod datasets show a consistent performance gain with SMOPE-Net over existing pose estimation methods. Here are links to our proposed SMOPE-Net, KITTI-6DoF dataset, and LabelImg3D labeling tool

Experimental Investigation on the Influence of Refrigerant Charge on the Performance of Trans-critical CO2 Water-Water Heat Pump

Natural refrigerant CO2 is widely used in refrigeration and heat pump systems. It is of great significance for reductions of ozone depletion and global warming. The efficiency of heat pump system is affected by many factors. The amount of refrigerant charge in the heat pump is a primary parameter that influences the energy efficiency. Undercharge or overcharge of refrigerant degrade its performance and deteriorate system reliability. Therefore, heat pump should be charged with an optimum amount of refrigerant to achieve high performance. However, it is difficult to calculate the optimum charge accurately because of the various components and operating parameters. Â In this paper, a trans-critical CO2Â heat pump system is set up to investigate the influence of refrigerant charge on the performance of a small-sized heat pump water heater. The trans-critical CO2Â heat pump system is composed of a rotary compressor, a tube-in-tube evaporator and gas cooler, an electronic expansion valve (EEV), and an internal heat exchanger (IHX). The objective of this study is to analyze the characteristics of the CO2 heat pump under various refrigerant charging conditions. Therefore, the performance of the CO2Â system was measured and discussed on the basis of refrigerant charge amount. Â Based on the experimental results, the effects of refrigerant charge on the power of system, rejection pressure, evaporating pressure, mass flow rate and coefficient of performance (COP) were analyzed with different EEV openings. The experimental results show that the COP was strongly related to CO2 mass charge and that the formation of the trans-critical cycle depended on CO2 mass charge greatly. The pressures in the gas cooler and the evaporator increased with the rise of refrigerant charge. The compression ratio decreased with the increase of the refrigerant charge. The COP has a maximum value at a specific CO2 mass charge. Undercharged CO2 systems could result in a fast decrease of the heating capacity and COP. However, overcharged CO2 systems could cause an abrupt increase of compressor power consumption. The heating cycle displayed different characteristics with different CO2 mass charges. The optimum CO2 mass charges varied with different EEV openings, and it could be determined according to several parameters

DeepScaler: Holistic Autoscaling for Microservices Based on Spatiotemporal GNN with Adaptive Graph Learning

Autoscaling functions provide the foundation for achieving elasticity in the modern cloud computing paradigm. It enables dynamic provisioning or de-provisioning resources for cloud software services and applications without human intervention to adapt to workload fluctuations. However, autoscaling microservice is challenging due to various factors. In particular, complex, time-varying service dependencies are difficult to quantify accurately and can lead to cascading effects when allocating resources. This paper presents DeepScaler, a deep learning-based holistic autoscaling approach for microservices that focus on coping with service dependencies to optimize service-level agreements (SLA) assurance and cost efficiency. DeepScaler employs (i) an expectation-maximization-based learning method to adaptively generate affinity matrices revealing service dependencies and (ii) an attention-based graph convolutional network to extract spatio-temporal features of microservices by aggregating neighbors' information of graph-structural data. Thus DeepScaler can capture more potential service dependencies and accurately estimate the resource requirements of all services under dynamic workloads. It allows DeepScaler to reconfigure the resources of the interacting services simultaneously in one resource provisioning operation, avoiding the cascading effect caused by service dependencies. Experimental results demonstrate that our method implements a more effective autoscaling mechanism for microservice that not only allocates resources accurately but also adapts to dependencies changes, significantly reducing SLA violations by an average of 41% at lower costs.Comment: To be published in the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023

Inverse design of nano-photonic wavelength demultiplexer with a deep neural network approach

In this paper, we propose a pre-trained-combined neural network (PTCN) as a comprehensive solution to the inverse design of an integrated photonic circuit. By utilizing both the initially pre-trained inverse and forward model with a joint training process, our PTCN model shows remarkable tolerance to the quantity and quality of the training data. As a proof of concept demonstration, the inverse design of a wavelength demultiplexer is used to verify the effectiveness of the PTCN model. The correlation coefficient of the prediction by the presented PTCN model remains greater than 0.974 even when the size of training data is decreased to 17%. The experimental results show a good agreement with predictions, and demonstrate a wavelength demultiplexer with an ultra-compact footprint, a high transmission efficiency with a transmission loss of -2dB, a low reflection of -10dB, and low crosstalk around -7dB simultaneously

Video-assisted thoracic bronchial sleeve lobectomy with bronchoplasty for treatment of lung cancer confined to a single lung lobe: a case series of Chinese patients

BACKGROUND: The outcomes of video-assisted thoracic bronchial sleeve lobectomy (VABSL), a minimally invasive video-assisted thoracoscopic (VATS) lobectomy, are mostly unknown in Chinese patients. OBJECTIVES: To investigate operative and postoperative outcomes of VABSL in a cases series of Chinese patients with lung cancer. METHODS: Retrospective study of 9 patients (male:female 8:1; mean age 59.4 ± 17.6 years, ranging 21–79 years) diagnosed with lung cancer of a single lobe, treated with VABSL between March 2009 and November 2011, and followed up for at least 2 months (mean follow-up: 14.17 ± 12.91 months). Operative outcomes (tumor size, operation time, estimated blood loss and blood transfusion), postoperative outcomes (intensive care unit [ICU] stay, hospitalization length and pathological tumor stage), death, tumor recurrence and safety were assessed. RESULTS: Patients were diagnosed with carcinoid cancer (11.1%), squamous carcinoma (66.7%) or small cell carcinoma (22.2%), affecting the right (77.8%) or left (22.2%) lung lobes in the upper (55.6%), middle (11.1%) or lower (33.3%) regions. TNM stages were T2 (88.9%) or T3 (11.1%); N0 (66.7%), N1 (11.1%) or N2 (22.2%); and M0 (100%). No patient required conversion to thoracotomy. Mean tumor size, operation time and blood loss were 2.50 ± 0.75 cm, 203 ± 20 min and 390 ± 206 ml, respectively. Patients were treated in the ICU for 18.7 ± 0.7 hours, and overall hospitalization duration was 20.8 ± 2.0 days. No deaths, recurrences or severe complications were reported. CONCLUSIONS: VABSL surgery is safe and effective for treatment of lung cancer by experienced physicians, warranting wider implementation of VABSL and VATS training in China

Synthesis and physical properties of Ce2_2Rh3+δ_{3+\delta}Sb4_4 single crystals

Millimeter-sized Ce$_2$Rh$_{3+\delta}$Sb$_4$ ($\delta\approx 1/8$) single crystals were synthesized by a Bi-flux method and their physical properties were studied by a combination of electrical transport, magnetic and thermodynamic measurements. The resistivity anisotropy $\rho_{a,b}/\rho_{c}\sim2$, manifesting a quasi-one-dimensional electronic character. Magnetic susceptibility measurements confirm $\mathbf{ab}$ as the magnetic easy plane. A long-range antiferromagnetic transition occurs at $T_N=1.4$ K, while clear short-range ordering can be detected well above $T_N$. The low ordering temperature is ascribed to the large Ce-Ce distance as well as the geometric frustration. Kondo scale is estimated to be about 2.4 K, comparable to the strength of magnetic exchange. Ce$_2$Rh$_{3+\delta}$Sb$_4$, therefore, represents a rare example of dense Kondo lattice whose Ruderman-Kittel-Kasuya-Yosida exchange and Kondo coupling are both weak but competing.Comment: 7 pages, 4 figures, 2 table