Purpose
To investigate the usefulness of the positron emission tomography response criteria in solid tumors 1.0 (PERCIST1.0) for predicting tumor response to neoadjuvant chemotherapy and prognosis ...and determine whether PERCIST improvements are necessary for esophageal squamous cell carcinoma (ESCC) patients.
Patients and methods
We analyzed the cases of 177 ESCC patients and examined the association between PERCIST and their pathological responses. Associations of whole-PERCIST with progression-free survival (PFS) and overall survival (OS) were evaluated by a Kaplan-Meier analysis and Cox proportional hazards model. To investigate potential PERCIST improvements, we used the survival tree technique to understand patients’ prognoses.
Results
There were significant correlations between the pathologic response and PERCIST of primary tumor (
p
< 0.001). The optimal cutoff value of the primary tumors’ SULpeak response to classify pathologic responses was −50.0%. The diagnostic accuracy of SULpeak response was 87.3% sensitivity, 54.1% specificity, 68.9% accuracy, positive predictive value 60.5%, and negative predictive value 84.1%. Whole-PERCIST was significantly associated with PFS and OS. The survival tree results indicated that a high reduction of the whole SULpeak response was significantly correlated with the patients’ prognoses. The cutoff values for the separation of prognoses were − 52.5 for PFS and − 47.1% for OS.
Conclusion
PERCIST1.0 can help predict tumor responses and prognoses. However,
18
F-FDG-PET/CT tends to underestimate residual tumors in histopathological response evaluations. Modified PERCIST, in which the partial metabolic response is further classified by the SULpeak response (−50%), might be more appropriate than PERCIST1.0 for evaluating tumor responses and stratifying high-risk patients for recurrence and poor prognosis.
•A hybrid FDD strategy with DE-LSSVR model and EWMA control charts is proposed.•Differential evolution algorithm is adopted to optimize the parameters of reference models.•LSSVR is adopted to improve ...the accuracy of reference models.•EWMA control charts are used to reduce the Type II error.•The proposed strategy improves the FDD performances significantly.
Development of the fault detection and diagnosis (FDD) for chiller systems is very important for improving the equipment reliability and saving energy consumption. The results of FDD performance are strongly dependent on the accuracy of chiller models. Since the accuracy of the chiller models depends on the indefinite model parameters which are normally chosen by experiments or experiences, an accurate chiller model is difficult to build. Therefore, optimization of model parameters is very useful to increase the accuracy of chiller models. This paper presents a new FDD strategy for centrifugal chillers of building air-conditioning systems, which is the combination between the nonlinear least squares support vector regression (LSSVR) based on the differential evolution (DE) algorithm and the exponentially weighted moving average (EWMA) control charts. In this strategy, the nonlinear LSSVR, which is a reformulation of SVR model with better generalization performances, is adopted to develop the reference feature parameter models in a typical non-linear chiller system. The DE algorithm which is a real-coding optimal algorithm with powerful global searching capacity is employed to enhance the accuracy of LSSVR models. The exponentially weighted moving average (EWMA) control charts are introduced to improve the fault detection capability as well as to reduce the Type II errors in a t-statistics-based way. Six typical faults of the chiller from the real-time experimental data of ASHRAE RP-1043 project are chosen to validate proposed FDD methods. Comprehensive comparisons between the proposed method and two similarly previous studies are performed. The comparison results show that the proposed method has achieved significant improvement in accuracy and reliability, especially at low severity levels. The proposed DE-LSSVR-EWMA strategy is robust for fault detection and diagnosis in centrifugal chiller systems.
Targeting the nonlinear and nonstationary characteristics of vibration signal from fault roller bearing and scarcity of fault samples, a novel method is presented and applied to roller bearing fault ...diagnosis in this paper. Firstly, the nonlinear and nonstationary vibration signal produced by local faults of roller bearing is decomposed into intrinsic scale components (ISCs) by using local characteristic-scale decomposition (LCD) method and initial feature vector matrices are obtained. Secondly, fault feature values are extracted by singular value decomposition (SVD) techniques to obtain singular values, while avoiding the selection of reconstruction parameters. Thirdly, a support vector machine (SVM) classifier based on Chemical Reaction Optimization (CRO) algorithm, called CRO-SVM method, is designed for classification of fault location. Lastly, the proposed method is validated by two experimental datasets. Experimental results show that the proposed method based LCD-SVD technique and CRO-SVM method have higher classification accuracy and shorter cost time than the comparative methods.
In this paper, a hybrid approach that combines a population-based method, adaptive elitist differential evolution (aeDE), with a powerful gradient-based method, spherical quadratic steepest descent ...(SQSD), is proposed and then applied for clustering analysis. This combination not only helps inherit the advantages of both the aeDE and SQSD but also helps reduce computational cost significantly. First, based on the aeDE’s global explorative manner in the initial steps, the proposed approach can quickly reach to a region that contains the global optimal value. Next, based on the SQSD’s locally effective exploitative manner in the later steps, the proposed approach can find the global optimal solution rapidly and accurately and hence helps reduce the computational cost. The proposed method is first tested over 32 benchmark functions to verify its robustness and effectiveness. Then, it is applied for clustering analysis which is one of the problems of interest in statistics, machine learning, and data mining. In this application, the proposed method is utilized to find the positions of the cluster centers, in which the internal validity measure is optimized. For both the benchmark functions and clustering problem, the numerical results show that the hybrid approach for aeDE (HaeDE) outperforms others in both accuracy and computational cost.
This paper studies the impacts of COVID-19 on the performance of the Vietnamese Stock Market-a rapidly growing emerging market in a country that has to date successfully controlled the disease ...outbreak. The study uses a random-effect model (REM) on panel data of stock returns of 733 listed companies on both HOSE (the Ho Chi Minh Stock Exchange) and HNX (the Hanoi Stock Exchange) from 2 January 2020 to 13 December 2020. The study shows that the number of daily COVID-19 confirmed cases in Vietnam has a negative impact on stock returns of listed companies in the market. The impacts were more severe for the pre-lockdown and second-wave period, compared to impact for the lockdown period. The impacts also differed across sectors, with the financial sector being the most affected. With significant government control and influence over the bank-dominated financial system, the financial sector was expected to absorb some of the negative shocks hitting the real sector. Such expectations were reflected in the stock market movement during the pandemic.
碩士
國立中央大學
能源工程研究所
106
In recent years, human demand for energy has risen steadily, and the use of fossil fuels has also produced pollutants and destroyed the environment. In order to get clean energy ...to protect environment. Developing fuel cell is of great urgency. In this study, the catalyst layers for polymer-electrolyte-membrane (PEM) fuel cells are fabricated by deposition of Pt3Co directly onto the gas diffusion layer (GDL) using pulsed laser deposition (PLD) to decrease the amount of Pt loading in PEMFC. Comparing with traditional production, using PLD can get more uniform catalyst layer to improve Mass Specific Power Density (MSPD).
The Current density at 0.6 V is 998.65 mA/cm2 in cathode side, using Pt3Co 100 μg/cm2 (Pt: 90.8 μg/cm2) with 2 hrs of dealloying process, the properties of electrochemistry and I-V performance are measured. We produce Pt-Shell/Pt3Co core Structure to promote Pt reactive surface area.The maximum current density at 0.6 V is 998 mA/cm2 were obtained for the sample with 2 hr de
The gradual depletion of oil resources and the necessity to reduce greenhouse gas emissions portray a concerning image of our contemporary security of liquid transportation fuels in the event of a ...global crisis. Despite a vast amount of natural gas resources that we have and the huge economic incentive, the conversion of gas to liquid fuels or chemicals is still very limited because of the high technological complexity and capital cost for facilities. However, with the anticipated depletion of liquid petroleum and the soaring price of crude oil, the conversion of natural gas to liquid feedstock or fuels will become more and more important. Higher alcohols are important feedstocks for the chemical and pharmaceutical industries and have wide applications as potential fuel additives or hydrogen carriers for fuel cells for clean energy delivery. There is a long-standing interest in the synthesis of higher alcohols from syngas, an important Fischer–Tropsch technology for natural gas conversion. The purpose of this review is to provide readers with an extensive account of catalytic synthesis of higher alcohols from syngas using various catalysts reviewed from a unique perspective: clarification of the active centers and reaction pathways. In light of the different sources providing the active centers, three major classes of catalysts in terms of monometallic, bimetallic, and trimetallic/multimetallic catalysts are systematically reviewed, and their respective performances are carefully compared. Finally, future works proposed to improve the catalyst design are described.
Creating large-scale entanglement lies at the heart of many quantum information processing protocols and the investigation of fundamental physics. For multipartite quantum systems, it is crucial to ...identify not only the presence of entanglement but also its detailed structure. This is because in a generic experimental situation with sufficiently many subsystems involved, the production of so-called genuine multipartite entanglement remains a formidable challenge. Consequently, focusing exclusively on the identification of this strongest type of entanglement may result in an all or nothing situation where some inherently quantum aspects of the resource are overlooked. On the contrary, even if the system is not genuinely multipartite entangled, there may still be many-body entanglement present in the system. An identification of the entanglement structure may thus provide us with a hint about where imperfections in the setup may occur, as well as where we can identify groups of subsystems that can still exhibit strong quantum-information-processing capabilities. However, there is no known efficient methods to identify the underlying entanglement structure. Here, we propose two complementary families of witnesses for the identification of such structures. They are based, respectively, on the detection of entanglement intactness and entanglement depth, each applicable to an arbitrary number of subsystems and whose evaluation requires only the implementation of solely two local measurements. Our method is also robust against noises and other imperfections, as reflected by our experimental implementation of these tools to verify the entanglement structure of five different eight-photon entangled states. In particular, we demonstrate how their entanglement structure can be precisely and systematically inferred from the experimental measurement of these witnesses. In achieving this goal, we also illustrate how the same set of data can be classically postprocessed to learn the most about the measured system.
Single-atom catalysts (SACs) have emerged as one of the most promising alternatives to noble metal-based catalysts for highly efficient oxygen reduction reaction (ORR). While SACs can offer notable ...benefits in terms of lowering overall catalyst cost, there is still room for improvement regarding catalyst activity. To this end, we designed and successfully fabricated an ORR electrocatalyst in which atomic clusters are embedded in an atomically dispersed Fe–N–C matrix (FeAC@FeSA–N–C), as shown by comprehensive measurements using aberration-corrected scanning transmission electron microscopy (AC-STEM) and X-ray absorption spectroscopy (XAS). The half-wave potential of FeAC@FeSA–N–C is 0.912 V (versus reversible hydrogen electrode (RHE)), exceeding that of commercial Pt/C (0.897 V), FeSA–N–C (0.844 V), as well as the half-wave potentials of most reported non-platinum-group metal catalysts. The ORR activity of the designed catalyst stems from single-atom active centers but is markedly enhanced by the presence of Fe nanoclusters, as confirmed by both experimental measurements and theoretical calculations.
PDF
