Yuan Ze University

The phenomenon of mode-mixing caused by intermittence signals is an annoying problem in Empirical Mode Decomposition (EMD) method. The noise assisted method of Ensemble EMD (EEMD) has not only effectively resolved this problem but also generated a new one, which tolerates the residue noise in the signal reconstruction. Of course, the relative magnitude of the residue noise could be reduced with large enough ensemble, it would be too time consuming to implement. An improved algorithm of noise enhanced data analysis method is suggested in this paper. In this approach, the residue of added white noises can be extracted from the mixtures of data and white noises via pairs of complementary ensemble IMFs with positive and negative added white noises. Though this new approach yields IMF with the similar RMS noise as EEMD, it effectively eliminated residue noise in the IMFs. Numerical experiments were conducted to demonstrate the new approach and also illustrate the problems of mode splitting and translation.

Characteristics of physical activity are indicative of one's mobility level, latent chronic diseases and aging process. Accelerometers have been widely accepted as useful and practical sensors for wearable devices to measure and assess physical activity. This paper reviews the development of wearable accelerometry-based motion detectors. The principle of accelerometry measurement, sensor properties and sensor placements are first introduced. Various research using accelerometry-based wearable motion detectors for physical activity monitoring and assessment, including posture and movement classification, estimation of energy expenditure, fall detection and balance control evaluation, are also reviewed. Finally this paper reviews and compares existing commercial products to provide a comprehensive outlook of current development status and possible emerging technologies.

Liquid-phase adsorption removal of Cu2+, Co2+, Ni2+, Zn2+, and Pb2+ in the concentration range of 5-25 mg/L using low-cost banana and orange peel wastes was examined at 30 degrees C. Under comparable conditions, the amount of adsorption decreased in the order Pb2+ > Ni2+ > Zn2+ > Cu2+ > Co2+ for both adsorbents. The adsorption isotherms could be better described by the Freundlich equation. The amount of adsorption increased with increasing pH and reached a plateau at pH > 7, which was confirmed by the variations of zeta potentials. The application potential of such cellulose-based wastes for metal removal (up to 7.97 mg Pb2+ per gram of banana peel at pH 5.5) at trace levels appeared to be promising.

In this study, a high step-up converter with a coupled-inductor is investigated. In the proposed strategy, a coupled inductor with a lower-voltage-rated switch is used for raising the voltage gain (whether the switch is turned on or turned off). Moreover, a passive regenerative snubber is utilized for absorbing the energy of stray inductance so that the switch duty cycle can be operated under a wide range, and the related voltage gain is higher than other coupled-inductor-based converters. In addition, all devices in this scheme also have voltage-clamped properties and their voltage stresses are relatively smaller than the output voltage. Thus, it can select low-voltage low-conduction-loss devices, and there are no reverse-recovery currents within the diodes in this circuit. Furthermore, the closed-loop control methodology is utilized in the proposed scheme to overcome the voltage drift problem of the power source under the load variations. As a result, the proposed converter topology can promote the voltage gain of a conventional boost converter with a single inductor, and deal with the problem of the leakage inductor and demagnetization of transformer for a coupled-inductor-based converter. Some experimental results via examples of a proton exchange membrane fuel cell (PEMFC) power source and a traditional battery are given to demonstrate the effectiveness of the proposed power conversion strategy.

Emission mechanisms for OLEDs and their characteristics.

Dimensional sentiment analysis aims to recognize continuous numerical values in multiple dimensions such as the valencearousal (VA) space. Compared to the categorical approach that focuses on sentiment classification such as binary classification (i.e., positive and negative), the dimensional approach can provide more fine-grained sentiment analysis. This study proposes a regional CNN-LSTM model consisting of two parts: regional CNN and LSTM to predict the VA ratings of texts. Unlike a conventional CNN which considers a whole text as input, the proposed regional CNN uses an individual sentence as a region, dividing an input text into several regions such that the useful affective information in each region can be extracted and weighted according to their contribution to the VA prediction. Such regional information is sequentially integrated across regions using LSTM for VA prediction. By combining the regional CNN and LSTM, both local (regional) information within sentences and long-distance dependency across sentences can be considered in the prediction process. Experimental results show that the proposed method outperforms lexicon-based, regression-based, and NN-based methods proposed in previous studies.

“Going green” has become an important environmental issue in contemporary business practice worldwide. This study examined the influence of a number of factors on green innovation and the consequences in terms of performance. The stakeholder theory was adopted to observe the effects of each stakeholder on the green innovation practices of companies and to determine how green innovation practices influence environmental and business performance. A research model with eight hypotheses was proposed to determine the associations between the variables of interest. An empirical survey was conducted of 202 Taiwanese service and manufacturing companies. The survey found that pressure from competitors and the government, along with employee conduct, all had significant and positive effects on green innovation practices. Additionally, a moderating effect of innovation orientation existed only in the relationship between green product innovation practices and employee conduct. This study not only provides a systematic way to analyze the effects of green innovation practices but also suggests the best means for companies to adopt green innovation practices.

This correspondence proposes a systematic adaptive sliding-mode controller design for the robust control of nonlinear systems with uncertain parameters. An adaptation tuning approach without high-frequency switching is developed to deal with unknown but bounded system uncertainties. Tracking performance is guaranteed. System robustness, as well as stability, is proven by using the Lyapunov theory. The upper bounds of uncertainties are not required to be known in advance. Therefore, the proposed method can be effectively implemented. Experimental results demonstrate the effectiveness of the proposed control method.

Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop. In this task participants designed systems to identify substrings in sentences corresponding to gene name mentions. A variety of different methods were used and the results varied with a highest achieved F1 score of 0.8721. Here we present brief descriptions of all the methods used and a statistical analysis of the results. We also demonstrate that, by combining the results from all submissions, an F score of 0.9066 is feasible, and furthermore that the best result makes use of the lowest scoring submissions.

Photocatalytic conversion of carbon dioxide (CO(2)) to hydrocarbons such as methanol makes possible simultaneous solar energy harvesting and CO(2) reduction, two birds with one stone for the energy and environmental issues. This work describes a high photocatalytic conversion of CO(2) to methanol using graphene oxides (GOs) as a promising photocatalyst. The modified Hummer's method has been applied to synthesize the GO based photocatalyst for the enhanced catalytic activity. The photocatalytic CO(2) to methanol conversion rate on modified graphene oxide (GO-3) is 0.172 μmol g cat(-1) h(-1) under visible light, which is six-fold higher than the pure TiO(2).

This research integrates the technology acceptance model and concepts of experiential value to investigate factors that affect sustainable relationship behavior toward using augmented-reality interactive technology (ARIT). In line with consumers’ innovativeness, this research discovers that consumers’ level of cognitive innovativeness affects their sustainable relationship behaviors toward using ARIT. Online consumers with high cognitive innovativeness put more emphasis on usefulness, aesthetics, and service excellence presented by ARIT; in contrast, those with low cognitive innovativeness focus on playfulness and ease of use presented by ARIT.

This study develops a high-performance stand-alone photovoltaic (PV) generation system. To make the PV generation system more flexible and expandable, the backstage power circuit is composed of a high step-up converter and a pulsewidth-modulation (PWM) inverter. In the dc-dc power conversion, the high step-up converter is introduced to improve the conversion efficiency in conventional boost converters to allow the parallel operation of low-voltage PV arrays, and to decouple and simplify the control design of the PWM inverter. Moreover, an adaptive total sliding-mode control system is designed for the voltage control of the PWM inverter to maintain a sinusoidal output voltage with lower total harmonic distortion and less variation under various output loads. In addition, an active sun tracking scheme without any light sensors is investigated to make the PV modules face the sun directly for capturing the maximum irradiation and promoting system efficiency. Experimental results are given to verify the validity and reliability of the high step-up converter, the PWM inverter control, and the active sun tracker for the high-performance stand-alone PV generation system.

BACKGROUND: Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication that results in increased morbidity and mortality after cardiac surgery. Most established prediction models are limited to the analysis of nonlinear relationships and fail to fully consider intraoperative variables, which represent the acute response to surgery. Therefore, this study utilized an artificial intelligence-based machine learning approach thorough perioperative data-driven learning to predict CSA-AKI. METHODS: A total of 671 patients undergoing cardiac surgery from August 2016 to August 2018 were enrolled. AKI following cardiac surgery was defined according to criteria from Kidney Disease: Improving Global Outcomes (KDIGO). The variables used for analysis included demographic characteristics, clinical condition, preoperative biochemistry data, preoperative medication, and intraoperative variables such as time-series hemodynamic changes. The machine learning methods used included logistic regression, support vector machine (SVM), random forest (RF), extreme gradient boosting (XGboost), and ensemble (RF + XGboost). The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC). We also utilized SHapley Additive exPlanation (SHAP) values to explain the prediction model. RESULTS: Development of CSA-AKI was noted in 163 patients (24.3%) during the first postoperative week. Regarding the efficacy of the single model that most accurately predicted the outcome, RF exhibited the greatest AUC (0.839, 95% confidence interval [CI] 0.772-0.898), whereas the AUC (0.843, 95% CI 0.778-0.899) of ensemble model (RF + XGboost) was even greater than that of the RF model alone. The top 3 most influential features in the RF importance matrix plot were intraoperative urine output, units of packed red blood cells (pRBCs) transfused during surgery, and preoperative hemoglobin level. The SHAP summary plot was used to illustrate the positive or negative effects of the top 20 features attributed to the RF. We also used the SHAP dependence plot to explain how a single feature affects the output of the RF prediction model. CONCLUSIONS: In this study, machine learning methods were successfully established to predict CSA-AKI, which determines risks following cardiac surgery, enabling the optimization of postoperative treatment strategies to minimize the postoperative complications following cardiac surgeries.

In this paper, a high-efficiency dc-dc converter with high voltage gain and reduced switch stress is proposed. Generally speaking, the utilization of a coupled inductor is useful for raising the step-up ratio of the conventional boost converter. However, the switch surge voltage may be caused by the leakage inductor so that it will result in the requirement of high-voltage-rated devices. In the proposed topology, a three-winding coupled inductor is used for providing a high voltage gain without extreme switch duty-cycle and enhancing the utility rate of magnetic core. Moreover, the energy in the leakage inductor is released directly to the output terminal for avoiding the phenomenon of circulating current and the production of switch surge voltage. In addition, the delay time formed with the cross of primary and secondary currents of the coupled inductor is manipulated to alleviate the reverse-recovery current of the output diode. It can achieve the aim of high-efficiency power conversion. Furthermore, the closed-loop control methodology is utilized in the proposed scheme to overcome the voltage drift problem of the power source under the variation of loads. Some experimental results via an example of a proton exchange membrane fuel cell power source with 250-W nominal rating are given to demonstrate the effectiveness of the proposed power conversion strategy

This paper presents an automatic traffic surveillance system to estimate important traffic parameters from video sequences using only one camera. Different from traditional methods that can classify vehicles to only cars and noncars, the proposed method has a good ability to categorize vehicles into more specific classes by introducing a new "linearity" feature in vehicle representation. In addition, the proposed system can well tackle the problem of vehicle occlusions caused by shadows, which often lead to the failure of further vehicle counting and classification. This problem is solved by a novel line-based shadow algorithm that uses a set of lines to eliminate all unwanted shadows. The used lines are devised from the information of lane-dividing lines. Therefore, an automatic scheme to detect lane-dividing lines is also proposed. The found lane-dividing lines can also provide important information for feature normalization, which can make the vehicle size more invariant, and thus much enhance the accuracy of vehicle classification. Once all features are extracted, an optimal classifier is then designed to robustly categorize vehicles into different classes. When recognizing a vehicle, the designed classifier can collect different evidences from its trajectories and the database to make an optimal decision for vehicle classification. Since more evidences are used, more robustness of classification can be achieved. Experimental results show that the proposed method is more robust, accurate, and powerful than other traditional methods, which utilize only the vehicle size and a single frame for vehicle classification.

Global issues such as environmental problems and food security are currently of concern to all of us. Circular bioeconomy is a promising approach towards resolving these global issues. The production of bioenergy and biomaterials can sustain the energy-environment nexus as well as substitute the devoid of petroleum as the production feedstock, thereby contributing to a cleaner and low carbon environment. In addition, assimilation of waste into bioprocesses for the production of useful products and metabolites lead towards a sustainable circular bioeconomy. This review aims to highlight the waste biorefinery as a sustainable bio-based circular economy, and, therefore, promoting a greener environment. Several case studies on the bioprocesses utilising waste for biopolymers and bio-lipids production as well as bioprocesses incorporated with wastewater treatment are well discussed. The strategy of waste biorefinery integrated with circular bioeconomy in the perspectives of unravelling the global issues can help to tackle carbon management and greenhouse gas emissions. A waste biorefinery-circular bioeconomy strategy represents a low carbon economy by reducing greenhouse gases footprint, and holds great prospects for a sustainable and greener world.

Purpose The rapid evolution in artificial intelligence (AI) has redefined the customer experience and created huge opportunities for companies to interact with customers using chatbots. This study explores the role of AI chatbots in influencing the online customer experience and customer satisfaction in e-retailing. Design/methodology/approach A research model based on the technology acceptance model and information system success model is proposed to describe the interrelationships among chatbot adoption, online customer experience and customer satisfaction. Personality is a moderator in the model. The authors used a quantitative approach to collect 425 useable online questionnaires and Statistical Product and Service Solutions (SPSS) and SmartPLS to analyze the measurement model and proposed hypotheses. Findings The usability of the chatbot had a positive influence on extrinsic values of customer experience, whereas the responsiveness of the chatbot had a positive impact on intrinsic values of customer experience. Furthermore, online customer experience had a positive relationship with customer satisfaction, and personality influenced the relationship between the usability of the chatbot and extrinsic values of customer experience. Originality/value This research extends understanding of the online customer experience with chatbots in e-retailing and provides empirical evidence by showing that extrinsic and intrinsic values of online customer experience are enhanced by chatbot adoption.

Transcription factors (TFs) are sequence-specific DNA-binding proteins acting as critical regulators of gene expression. The Plant Promoter Analysis Navigator (PlantPAN; http://PlantPAN2.itps.ncku.edu.tw) provides an informative resource for detecting transcription factor binding sites (TFBSs), corresponding TFs, and other important regulatory elements (CpG islands and tandem repeats) in a promoter or a set of plant promoters. Additionally, TFBSs, CpG islands, and tandem repeats in the conserve regions between similar gene promoters are also identified. The current PlantPAN release (version 2.0) contains 16 960 TFs and 1143 TF binding site matrices among 76 plant species. In addition to updating of the annotation information, adding experimentally verified TF matrices, and making improvements in the visualization of transcriptional regulatory networks, several new features and functions are incorporated. These features include: (i) comprehensive curation of TF information (response conditions, target genes, and sequence logos of binding motifs, etc.), (ii) co-expression profiles of TFs and their target genes under various conditions, (iii) protein-protein interactions among TFs and their co-factors, (iv) TF-target networks, and (v) downstream promoter elements. Furthermore, a dynamic transcriptional regulatory network under various conditions is provided in PlantPAN 2.0. The PlantPAN 2.0 is a systematic platform for plant promoter analysis and reconstructing transcriptional regulatory networks.

This paper uses an ant colony meta-heuristic optimization method to solve the redundancy allocation problem (RAP). The RAP is a well known NP-hard problem which has been the subject of much prior work, generally in a restricted form where each subsystem must consist of identical components in parallel to make computations tractable. Meta-heuristic methods overcome this limitation, and offer a practical way to solve large instances of the relaxed RAP where different components can be placed in parallel. The ant colony method has not yet been used in reliability design, yet it is a method that is expressly designed for combinatorial problems with a neighborhood structure, as in the case of the RAP. An ant colony optimization algorithm for the RAP is devised & tested on a well-known suite of problems from the literature. It is shown that the ant colony method performs with little variability over problem instance or random number seed. It is competitive with the best-known heuristics for redundancy allocation.

Abstract In this study, we demonstrated a blue phosphorescent organic light-emitting diode (BPOLED) based on a host with two carbazole and one trizole (2CbzTAZ) moiety, 9,9′-(2-(4,5-diphenyl-4H-1,2,4-triazol-3-yl)-1,3-phenylene)bis(9H-carbazole), that exhibits bipolar transport characteristics. Compared with the devices with a carbazole host (N,N’-dicarbazolyl-3,5-benzene, (mCP)), triazole host (3-(biphenyl-4-yl)-5-(4-tert-butylphenyl)-4-phenyl-4H-1,2,4-triazole, (TAZ)), or a physical mixture of mCP:TAZ, which exhibit hole, electron, and bipolar transport characteristics, respectively, the BPOLED with the bipolar 2CbzTAZ host exhibited the lowest driving voltage (6.55 V at 10 mA/cm 2 ), the highest efficiencies (maximum current efficiency of 52.25 cd/A and external quantum efficiency of 23.89%), and the lowest efficiency roll-off, when doped with bis[2-(4,6-difluorophenyl)pyridinato-C2,N](picolinato)iridium(III) (FIrpic) as blue phosphor. From analyses of light leakage of the emission spectra of electroluminescence, transient electroluminescence, and partially doped OLEDs, it was found that the recombination zone was well confined inside the emitting layer and the recombination rate was most efficient in a 2CbzTAZ-based OLED. For the other cases using mCP, TAZ, and mCP:TAZ as hosts, electrons and holes transported with different routes that resulted in carrier accumulation on different organic molecules and lowered the recombination rate.