Optical effects of spacecraft-environment interaction Spectrometric observations by the DE-B satellite

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76162/1/AIAA-1983-2657-139.pd

Optical effects of spacecraft-environment interaction Spectrometric observations of the DE-2 satellite

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76678/1/AIAA-25728-876.pd

Atomic oxygen in the mesosphere and lower thermosphere derived from SABER : Algorithm theoretical basis and measurement uncertainty

Peer reviewedPublisher PD

Hybridization of harmonic search algorithm in training radial basis function with dynamic decay adjustment for condition monitoring

In recent decades, hybridization of superior attributes of few algorithms was proposed to aid in covering more areas of complex application as well as improves performance. Condition monitoring is a major component in predictive maintenance which monitors the condition and identifies significant changes in the machinery parameter to perform early detection and prevent equipment defects that could cause unplanned downtime or incur unnecessary expenditures. An effective condition monitoring model is helpful to reduce the frequency of unexpected breakdown incidents and thus, facilitates in maintenance. ANN has shown effective in various condition monitoring and fault detection applications. ANN is popular due to its capability of identifying the complex nonlinear relationships among features in a large dataset and hence, it can perform with an accurate prediction. However, a drawback is that the performance of ANN is sensitive to the parameters (i.e., number of hidden neurons and the initial values of connection weights) in its architecture where the settings of these parameters are subject to tuning on a trial-and-error basis. Hence, a wide range of studies have been focused on determining the optimal weight values of ANN models and the number of hidden neurons. In this research work, the motivation is to develop an autonomous learning model based on the hybridization of an adaptive ANN and a metaheuristic algorithm for optimizing ANN parameters so that the network could perform learning and adaptation in a more flexible way and handle condition classification tasks more accurately in industries, such as in power systems. This paper presents an intelligent system integrating a Radial Basis Function Network with Dynamic Decay Adjustment (RBFN-DDA) with a Harmony Search (HS) to perform condition monitoring in industrial processes. RBFN-DDA performs incremental learning wherein its structure expands by adding new hidden units to include new information. As such, its training can reach stability in a shorter time compared to the gradient-descent based methods. To achieve optimal RBFN-DDA performance, HS is proposed to optimize the center and the width of each hidden unit in a trained RBFN. By integrating with the HS algorithm, the proposed metaheuristic neural network (RBFN-DDA-HS) can optimize the RBFN-DDA parameters and improve classification performances from the original RBFN-DDA by 2.2% up to 22.5% in two benchmarks datasets, which are numerical records from a bearing and steel plate system and a condition-monitoring system in a power plant (i.e., the circulating water (CW) system). The results also show that the proposed RBFN-DDA-HS is compatible, if not better than, the classification performances of other state-of-the-art machine learning methods

Advances of metaheuristic algorithms in training neural networks for industrial applications

In recent decades, researches on optimizing the parameter of the artificial neural network (ANN) model has attracted significant attention from researchers. Hybridization of superior algorithms helps improving optimization performance and capable of solving complex applications. As a traditional gradient-based learning algorithm, ANN suffers from a slow learning rate and is easily trapped in local minima when training techniques such as gradient descent (GD) and back-propagation (BP) algorithm are used. The characteristics of randomization and selection of the best or near-optimal solution of metaheuristic algorithm provide an effective and robust solution; therefore, it has always been used in training of ANN to improve and overcome the above problems. New metaheuristic algorithms are proposed every year. Therefore, the review of its latest developments is essential. This article attempts to summarize the metaheuristic algorithms which have been proposed from the year 1975 to 2020 from various journals, conferences, technical papers, and books. The comparison of the popularity of the metaheuristic algorithm is presented in two time frames, such as algorithms proposed in the recent 20 years and those proposed earlier. Then, some of the popular metaheuristic algorithms and their working principle are reviewed. This article further categorizes the latest metaheuristic search algorithm in the literature to indicate their efficiency in training ANN for various industry applications. More and more researchers tend to develop new hybrid optimization tools by combining two or more metaheuristic algorithms to optimize the training parameters of ANN. Generally, the algorithm’s optimal performance must be able to achieve a fine balance of their exploration and exploitation characteristics. Hence, this article tries to compare and summarize the properties of various metaheuristic algorithms in terms of their convergence rate and the ability to avoid the local minima. This information is useful for researchers working on algorithm hybridization by providing a good understanding of the convergence rate and the ability to find a global optimum

Secondary Primary Malignancy Risk in Patients With Cervical Cancer in Taiwan: A Nationwide Population-Based Study.

To evaluate the risk of secondary primary malignancy (SPM) in patients with cervical cancer using a nationwide population-based dataset.Patients newly diagnosed with cervical cancer between 1997 and 2011 were identified using Taiwan's National Health Insurance database. Patients with antecedent malignancies were excluded. Standardized incidence ratios (SIRs) for SPM were calculated by comparing with the cancer incidence in the general population. Risk factors for cancer development were analyzed using Cox proportional hazard models.During the 14-year study period (follow-up of 223,062 person-years), 2004 cancers developed in 35,175 patients with cervical cancer. The SIR for all cancers was 1.56 (95% confidence interval, 1.50-1.63, P < 0.001). SIRs for follow-up periods of >10, 5 to 10, 1 to 5, and <1 year were 1.37, 1.51, 1.34, and 2.59, respectively. After the exclusion of SPM occurring within 1 year of cervical cancer diagnosis, SIRs were significantly higher for cancers of the esophagus (2.05), stomach (1.38), colon, rectum, and anus (1.36); lung and mediastinum (2.28), bone and soft tissue (2.23), uterus (3.76), bladder (2.26), and kidneys (1.41). Multivariate analysis showed that age ≥60 was a significant SPM risk factor (hazard ratio [HR] 1.59). Different treatments for cervical cancer, including radiotherapy (HR 1.41) and chemotherapy (HR 1.27), had different impacts on SPM risk. Carboplatin and fluorouracil independently increased SPM risk in cervical cancer patients.Patients with cervical cancer are at increased risk of SPM development. Age ≥60 years, chemotherapy, and radiotherapy are independent risk factors. Carboplatin and fluorouracil also increased SPM risk independently. Close surveillance of patients at high risk should be considered for the early detection of SPMs

Secondary Primary Malignancy Risk in Patients With Ovarian Cancer in Taiwan: A Nationwide Population-Based Study.

To evaluate the incidence of secondary primary malignancy (SPM) in patients with ovarian cancer using a nationwide retrospective population-based dataset. Patients newly diagnosed with ovarian cancer between 1997 and 2010 were identified using Taiwan's National Health Insurance database. Patients with antecedent malignancies were excluded. Standardized incidence ratios (SIRs) for SPM were calculated and compared with the cancer incidence in the general population. Risk factors for cancer development were analyzed using Cox proportional hazard models. Effects of surgery, chemotherapy, and radiotherapy after ovarian cancer diagnosis were regarded as time-dependent variables to prevent immortal time bias. During the 14-year study period (follow-up of 56,214 person-years), 707 cancers developed in 12,127 patients with ovarian cancer. The SIR for all cancers was 2.78 (95% confidence interval 2.58-3.00). SIRs for follow-up periods of >5, 1-5, and <1 year were 1.87, 2.04, and 6.40, respectively. After the exclusion of SPM occurring within 1 year of ovarian cancer diagnosis, SIRs were significantly higher for cancers of the colon, rectum, and anus (2.14); lung and mediastinum (1.58); breast (1.68); cervix (1.65); uterus (7.96); bladder (3.17), and thyroid (2.23); as well as for leukemia (3.98) and others (3.83). Multivariate analysis showed that age ≥ 50 years was a significant SPM risk factor (hazard ratio [HR] 1.60). Different treatments for ovarian cancer, including radiotherapy (HR 2.07) and chemotherapy (HR 1.27), had different impacts on SPM risk. Patients with ovarian cancer are at increased risk of SPM development. Age ≥ 50 years, radiotherapy, and chemotherapy are independent risk factors. Close surveillance of patients at high risk should be considered for the early detection of SPM

Secondary Primary Malignancy Risk in Patients With Ovarian Cancer in Taiwan: A Nationwide Population-Based Study.

To evaluate the incidence of secondary primary malignancy (SPM) in patients with ovarian cancer using a nationwide retrospective population-based dataset. Patients newly diagnosed with ovarian cancer between 1997 and 2010 were identified using Taiwan's National Health Insurance database. Patients with antecedent malignancies were excluded. Standardized incidence ratios (SIRs) for SPM were calculated and compared with the cancer incidence in the general population. Risk factors for cancer development were analyzed using Cox proportional hazard models. Effects of surgery, chemotherapy, and radiotherapy after ovarian cancer diagnosis were regarded as time-dependent variables to prevent immortal time bias. During the 14-year study period (follow-up of 56,214 person-years), 707 cancers developed in 12,127 patients with ovarian cancer. The SIR for all cancers was 2.78 (95% confidence interval 2.58-3.00). SIRs for follow-up periods of >5, 1-5, and <1 year were 1.87, 2.04, and 6.40, respectively. After the exclusion of SPM occurring within 1 year of ovarian cancer diagnosis, SIRs were significantly higher for cancers of the colon, rectum, and anus (2.14); lung and mediastinum (1.58); breast (1.68); cervix (1.65); uterus (7.96); bladder (3.17), and thyroid (2.23); as well as for leukemia (3.98) and others (3.83). Multivariate analysis showed that age ≥ 50 years was a significant SPM risk factor (hazard ratio [HR] 1.60). Different treatments for ovarian cancer, including radiotherapy (HR 2.07) and chemotherapy (HR 1.27), had different impacts on SPM risk. Patients with ovarian cancer are at increased risk of SPM development. Age ≥ 50 years, radiotherapy, and chemotherapy are independent risk factors. Close surveillance of patients at high risk should be considered for the early detection of SPM

A generational comparison for unfavorable cancer of unknown primary in a single institute over 20 years

Abstract Background The prognosis of unfavorable cancer of unknown primary is extremely poor. This is the first report to compared the treatment results between generations of CUP and examined prognostic factors. Methods This retrospective single‐center cohort study enrolled 68 patients with newly diagnosed unfavorable cancer of unknown primary at Taipei Veteran General Hospital from 2017 to 2020 as study cohort and 167 patients from 2000 to 2009 as historical cohort. Results The median overall survival was 4.3 months in the study cohort (95% CI, 2.7–6.2 months) and 4.5 months in the historical cohort (95% CI, 3.0–5.5 months; p = 0.858). Eleven patients in the study cohort received immunotherapy. The disease control rates were 45%. Multivariate analysis showed that an Eastern Cooperative Oncology Group score > 1 and a C‐reactive protein level > 1 correlated with poor survival. A new prognostic stratification model was constructed by using Eastern Cooperative Oncology Group score and C‐reactive protein values. The good‐, intermediate‐, and poor‐risk groups had distinct median overall survival of 18.3, 7.0 and 1.2 months, respectively (area under the curve, 0.817; p < 0.001). Conclusion The outcome of unfavorable cancer of unknown primary has not changed much over the last 20 years. The application of a new prognostic stratification model can further stratify unfavorable cancer of unknown primary