This study proposes a novel evolutionary kernel intuitionistic fuzzy c-means clustering algorithm (EKIFCM) that combines Atanassov's intuitionistic fuzzy sets (IFSs) with kernel-based fuzzy c-means ...(KFCM), and genetic algorithms (GA) are optimally used simultaneously to select the parameters of the EKIFCM. The EKIFCM can obtain the advantages of intuitionistic fuzzy sets, kernel functions, and GA in actual clustering problems. Experiments on 2-D synthetic datasets and machine learning repository (http://archive.ics.uci.edu/beta/) datasets show that the proposed EKIFCM is more efficient than conventional algorithms such as the k-means (KM), FCM, Gustafson-Kessel (GK) clustering algorithm, Gath-Geva (GG) clustering algorithm, Chaira's intuitionistic fuzzy c-means (IFCM), and kernel-based fuzzy c-means with Gaussian kernel functions KFCM(G) in standard measurement indexes.
An investigation is performed into the efficiency of the
Streptomyces griseus
HUT 6037 enzyme immobilized in three different mesoporous silicas, namely mesoporous silica film, mesocellular foam, and ...rod-like SBA-15. It is shown that for all three supports, the pH value changes the surface charge and charge density and hence determines the maximum loading capacity of the enzyme. The products of the enzyme hydrolytic reaction are analyzed by
1
H-NMR. The results show that among the three silica supports, the mesoporous silica film (with a channel length in the range of 60-100 nm) maximizes the accessibility of the immobilized enzyme. The loading capacity of the enzyme is up to 95% at pH 7 and the activity of the immobilized enzyme is maintained for more than 15 days when using a silica film support. The order of the activity of the enzyme immobilized in different mesoporous silica supports is: mesoporous silica film > mesocellular foam > rod-like SBA-15. Furthermore, the immobilized enzyme can be easily separated from the reaction solution
via
simple filtration or centrifugation methods and re-used for hydrolytic reaction as required.
Mesoporous silica films were used as supports with high loading capacity and enzyme activity.
Aim
This systematic review and network meta‐analysis aimed to evaluate the efficacy of adjunctive locally delivered antimicrobials, compared to subgingival instrumentation alone or plus a placebo, on ...changes in probing pocket depth (PPD) and clinical attachment level (CAL), in patients with residual pockets during supportive periodontal care.
Materials and methods
Literature search was performed with electronic databases and by hand until 31 May 2020. Primary outcome was the changes in PPD. The treatment effects between groups were estimated with weighted mean differences (WMD) with 95% confidence intervals (CI) and prediction intervals (PI) by using random‐effects network meta‐analysis.
Results
Twenty‐two studies were included. Significantly greater PPD reduction was achieved in chlorhexidine chip group (WMD: 0.65 mm, 95% CI: 0.21–1.10) and tetracycline fibre group (WMD: 0.64 mm, 95% CI: 0.20–1.08) over 6‐month follow‐up. Other adjunctive antimicrobial agents achieved non‐significant improvements compared to scaling and root planing alone. All differences between adjunctive therapies were statistically non‐significant. Similar findings were observed for CAL gain.
Conclusion
Adjunctive local antimicrobial agents achieved small additional PPD reduction and CAL gain in residual pockets for a follow‐up of up to 6 months. Tetracycline fibre and chlorhexidine chip achieved better results than other antimicrobials.
Purpose
The purpose of this paper is to establish mechanisms for process improvement so that production efficiency and product quality can be expected, and create a sustainable development in terms ...of circular economy.
Design/methodology/approach
The authors obtain a critical value from statistical hypothesis testing, and thereby construct a process capability indices chart, which both lowers the chance of quality level misjudgment caused by sampling error and provides reference for the processes improvement in poor quality levels. The authors used the bottom bracket of bicycles as an example to demonstrate the model and methods proposed in this study.
Findings
This approach enables us to plot multiple quality characteristics, despite varying attributes and specifications, onto the same process capability analysis chart. And it therefore increases accuracy and precision to reduce rework and scrap rates (reduce), increase product availability, reduce maintenance frequency and increase reuse (reuse), increase the recycle rates of components (recycle) and lengthen service life, which will delay recovery time (recovery).
Originality/value
Parts manufacturers in the industry chain can upload their production data to the cloud platform. The quality control center of the bicycle manufacturer can utilized the production data analysis model to identify critical-to-quality characteristics. The platform also offers reference for improvement and adds the improvement achievements and experience to its knowledge management to provide the entire industry chain. Feedback is also given to the R&D department of the bicycle manufacturer as reference for more robust product designs, more reasonable tolerance designs, and selection criteria for better parts suppliers, thereby forming an intelligent manufacturing loop system.
This study introduces a recent field experiment investigating multiscale terrain–circulation–precipitation interactions. When a synoptic‐scale northeasterly wind prevails under the active East Asian ...winter monsoon, stratocumulus cloud decks with severe rainfall exceeding 100 mm·day−1 frequently occur in the northeastern plain area and adjacent mountains in Yilan, Taiwan. The Yilan Experiment of Severe Rainfall (YESR2020) is a field campaign from November 20, 2020, to November 24, 2020, to survey the physical processes leading to severe wintertime rainfall. The three‐dimensional structure of the wind field and the atmospheric environment can be identified through high temporal and spatial resolution sounding observations, which is empowered by the novel Storm Tracker mini‐radiosonde. During YESR2020, the continuously collected meteorological data of two northeasterly episodes captured the variability of local‐scale wind patterns and the features of the severe rainfall induced by stratocumulus. A preliminary analysis indicated that a local‐scale convergence line could appear over the plain area of Yilan under the northeasterly environmental condition. The precipitation hotspot was located in the mountain region of southern Yilan, where the local winds signified turbulence features. Moreover, the severe rainfall of the two northeasterly episodes spotlighted shallow cumulus under stratus with pure warm rain processes. The results of YESR2020 inspire the arrangement of future field observations to explore detailed mechanisms of heavily precipitating stratocumulus over complex topography.
We conducted the Yilan Experiment of Severe Rainfall (YESR2020) to survey physical processes leading to severe rainfall in the northeastern plain area and adjacent mountains in Yilan, Taiwan, when a synoptic‐scale northeasterly wind prevails under the active East Asian winter monsoon with stratocumulus cloud decks. A preliminary analysis indicated that a local‐scale convergence line appeared over the plain area, and the precipitation hotspot was located in the mountain region of southern Yilan, where turbulence features were apparent. The results inspire the arrangement of future field observations to explore mechanisms of heavily precipitating stratocumulus over complex topography.
Wind is one of the most essential sources of clean, renewable energy, and is, therefore, a critical element in responsible power consumption and production. The accurate prediction of wind speed ...plays a key role in decision-making and management in wind power generation. This study proposes a model using a deep belief network with genetic algorithms (DBNGA) for wind speed forecasting. The genetic algorithms are used to determine parameters for deep belief networks. Wind speed and weather-related data are collected from Taiwan's central weather bureau for this purpose. This paper uses both time series data and multivariate regression data to forecast wind speed. The seasonal autoregressive integrated moving average (SARIMA) method and the least squares support vector regression for time series with genetic algorithms (LSSVRTSGA) are used to forecast wind speed in a time series, and the least squares support vector regression with genetic algorithms (LSSVRGA) and DBNGA models are used to predict wind speed in a multivariate format. The empirical results show that forecasting wind speed by the DBNGA models outperforms the other forecasting models in terms of forecasting accuracy. Thus, the DBNGA model is a feasible and effective approach for wind speed forecasting.
Limited evidence exists regarding the socioeconomic inequalities in cerebrovascular disease (CBD) mortality at different urbanization levels. Therefore, this study was conducted to assess the ...socioeconomic inequalities and urbanization levels in township-based CBD mortality in Taiwan.
Socioeconomic variables, including the percentages of low-income households, individuals with a university education and above, and tax payments, were measured at the township level from 2011 to 2020. Urbanization was also determined by the national survey and divided into seven levels. Age-standardized mortality rate (ASMR) of CBD was calculated using a Geographic Information System (GIS) in 358 townships. The effects of socioeconomic variables and urbanization levels on relative and absolute inequalities in township-based CBD mortality rates were examined.
Significant differences in ASMR of CBD were observed across all socioeconomic status indicators over the years. Higher proportions of low-income households were associated with higher ASMR of CBD. Conversely, there were negative correlations between higher proportions of individuals with a university education and above and tax payments with ASMR of CBD. The regression analysis indicated significant impacts of relative and absolute socioeconomic inequalities on ASMR of CBD. Additionally, a moderation effect of socioeconomic variables and urbanization on CBD mortality rates was observed, with rural areas showing sensitivity to these factors.
Although ASMR of CBD showed significant decreases over time, socioeconomic inequalities in CBD mortality rates persist. Interventions targeting socioeconomic inequalities in health outcomes, especially in rural areas, are needed to address this issue.
Stepped-impedance resonators with different dimensions are used to design bandpass filters with a dual-passband response, as well as good rejection levels in the extended upper stopband. To achieve ...the goal, the resonators are designed to have two identical leading resonant frequencies, but dispersed higher order ones to make spurious peaks have low levels and small bandwidths. The stopband is then extended and the rejection levels are enhanced by collocating transmission zeros with the unwanted peaks. The zeros are tuned by adjusting the coupling lengths of the coupled stages and sliding the tap positions of the dual-band transformers along the end resonators. Measured results of two experimental circuits show a rejection level of 30 dB up to more than eight times the first passband frequency can be obtained. The measured data have good agreement with the simulation.
The use of polyacrylonitrile (PAN) as a host for gel polymer electrolytes (GPEs) commonly produces a strong dipole–dipole interaction with the polymer. This study presents a strategy for the ...application of PAN in GPEs for the production of high performance lithium ion batteries. The resulting gel electrolyte GPE-AVM comprises a poly(acrylonitrile-co-vinyl acetate) copolymer blending poly(methyl methacrylate) as a host, which is swelled using a liquid electrolyte (LE) of 1 M LiPF6 in carbonate solvent. Vinyl acetate and methacrylate groups segregate the PAN chains in the GPE, which produces high ionic conductivity (3.5 × 10 –3 S cm–1 at 30 °C) and a wide electrochemical voltage range (>6.5 V) as well as an excellent Li+ transference number of 0.6. This study includes GPE-AVM in a full-cell battery comprising a LiFePO4 cathode and graphite anode to promote ion motion, which reduced resistance in the battery by 39% and increased the specific power by 110%, relative to the performance of batteries based on LE. The proposed GPE-based battery has a capacity of 140 mAh g–1 at a discharge rate of 0.1 C and is able to deliver 67 mAh g–1 of electricity at 17 C. The proposed GPE-AVM provides a robust interface with the electrodes in full-cell batteries, resulting in 93% capacity retention after 100 charge–discharge cycles at 17 C and 63% retention after 1000 cycles.
Long-term wind power forecasting is a challenging endeavor that requires predictions that span years into the future. Accurate forecasting is crucial for optimizing energy production, grid ...integration, maintenance scheduling, and financial planning. This study attempts to first develop the long short-term memory networks (LSTM) with a seasonal wavelet transform forecasting model for practical long-term wind power forecasting problems with seasonal and regional influences on wind power and the instability of data signals. This model encapsulates wavelet transformation and seasonal decomposition, and employs LSTM for forecasting. The new prediction model adopted seasonal decompositions and two LSTMs to approach low- and high-frequency series datasets, as well as the wavelet synthesis prediction values. Furthermore, the parameters of the LSTM models are selected using stochastic optimization. For a comprehensive evaluation, the proposed LSTM with seasonal wavelet transform is compared with seven methods, including seasonal LSTM (SLSTM), wavelet LSTM (WLSTM), and the seasonal auto-regressive integrated moving average (SARIMA), back propagation neural network (BPNN), generalized regression neural network (GRNN), least square support vector regression (LSSVR), and support vector regression (SVR) were employed for long-term wind power output forecasting of wind farms. The empirical results underscore that the performance of the proposed forecasting model is better than other methods in terms of forecasting accuracy, which could efficiently provide reliable long-term predictions for long-term wind power output forecasting.
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