Forecast with Forecasts: Diversity Matters

Forecast combination has been widely applied in the last few decades to improve forecast accuracy. In recent years, the idea of using time series features to construct forecast combination model has flourished in the forecasting area. Although this idea has been proved to be beneficial in several forecast competitions such as the M3 and M4 competitions, it may not be practical in many situations. For example, the task of selecting appropriate features to build forecasting models can be a big challenge for many researchers. Even if there is one acceptable way to define the features, existing features are estimated based on the historical patterns, which are doomed to change in the future, or infeasible in the case of limited historical data. In this work, we suggest a change of focus from the historical data to the produced forecasts to extract features. We calculate the diversity of a pool of models based on the corresponding forecasts as a decisive feature and use meta-learning to construct diversity-based forecast combination models. A rich set of time series are used to evaluate the performance of the proposed method. Experimental results show that our diversity-based forecast combination framework not only simplifies the modelling process but also achieves superior forecasting performance.Comment: 23 pages, 4 figures, 4 table

A method for the suppression of fluctuations in the neutral-point potential of a three-level NPC inverter with a capacitor-voltage loop

This paper investigates the problem of fluctuation of the neutral-point potential (NPP) in a three-level NPC inverter with a capacitor-voltage loop. The phase pulse width duty cycle disturbance PWM method is proposed to suppress the NPP fluctuation efficiently. Based on the basic carrier-based Phase Disposition (PD) PWM method, the average pulse neutral-point current model is established. Then the frequency, amplitude and equivalent initial phase of the NPP fluctuation are analyzed based on the current model. According to the alternating error of the DC-link capacitor voltages, a capacitor-voltage loop with a quasi PR (proportional resonant) controller is presented. The control variable, which varies with the modulation index, phase current, load power factor, etc, can be obtained from the quasi PR controller. Finally, an experimental three-level NPC inverter is described and the validity and feasibility of the proposed method are verified by experimental results

Directive local color transfer based on dynamic look-up table

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Effect of Soy Lecithin, Glucose Oxidase, and Transglutaminase on Dough Rheology and Quality Properties of Steamed Bread Enriched with Potato Pulp

This study aimed to assess the effect of soy lecithin (Soy L, 0.2%–1.0%), glucose oxidase (GOX, 0.3–1.5 U/g), and transglutaminase (TG, 0.3–1.5 U/g) on dough elongation properties and texture qualities of steamed bread. The optimum formulation of steamed bread prepared from wheat flour (50%) and potato pulp (50%) was investigated. Results showed that Soy L and GOX significantly (P < 0.05) affected the specific volume and hardness of steamed bread, whereas TG significantly (P < 0.05) affected the resistance to extension and extensibility of dough, as well as the springiness and cohesiveness of steamed bread. The optimum formulation consisting of 0.65% Soy L, 0.92 U/g GOX, and 0.96 U/g TG increased the viscoelasticity and fermentation characteristics of dough and improved the specific volume, texture, and porosity of steamed bread enriched with potato pulp

Integration of scRNA-Seq and bulk RNA-Seq uncover perturbed immune cell types and pathways of Kawasaki disease

IntroductionKawasaki disease (KD) is an acute febrile illness primarily affecting children and characterized by systemic inflammation and vasculitis that can lead to coronary artery complications. The aim of this study was to gain a comprehensive understanding of immune dysregulation in KD.MethodsTo this end, we employed integration of single-cell RNA sequencing (scRNA-Seq) and bulk RNA sequencing (bulk RNA-Seq) data. Furthermore, we conducted flow cytometry analysis for a cohort of 82 KD patients.ResultsOur analysis revealed significant heterogeneity within immune cell populations in KD patients, with distinct clusters of T cells, B cells, and natural killer (NK) cells. Importantly, CD4+ naïve T cells in KD patients were found to predominantly differentiate into Treg cells and Th2 cells, potentially playing a role in the excessive inflammation and vascular damage characteristic of the disease. Dysregulated signaling pathways were also identified, including the mTOR signaling pathway, cardiomyopathy pathway, COVID-19 signaling pathway, and pathways involved in bacterial or viral infection.DiscussionThese findings provide insights into the immunopathogenesis of KD, emphasizing the importance of immune cell dysregulation and dysregulated signaling pathways. Integration of scRNA-Seq and bulk RNA-Seq data offers a comprehensive view of the molecular and cellular alterations in KD and highlights potential therapeutic targets for further investigation. Validation and functional studies are warranted to elucidate the roles of the identified immune cell types and pathways in KD pathogenesis and to develop targeted interventions to improve patient outcomes

Determination of key enzymes for threonine synthesis through in vitro metabolic pathway analysis

Figure S1. The pathway flux (J) in the in vitro system when one enzyme concentration was increased. (A) The pathway flux when purified ThrA was added to the crude enzyme extract. (B) The pathway flux when purified Asd was added to the crude enzyme extract. (C) The pathway flux when purified ThrB was added to the crude enzyme extract. (D) The pathway flux when purified ThrC was added to the crude enzyme extract

Wideband Power Spectrum Sensing: a Fast Practical Solution for Nyquist Folding Receiver

The limited availability of spectrum resources has been growing into a critical problem in wireless communications, remote sensing, and electronic surveillance, etc. To address the high-speed sampling bottleneck of wideband spectrum sensing, a fast and practical solution of power spectrum estimation for Nyquist folding receiver (NYFR) is proposed in this paper. The NYFR architectures is can theoretically achieve the full-band signal sensing with a hundred percent of probability of intercept. But the existing algorithm is difficult to realize in real-time due to its high complexity and complicated calculations. By exploring the sub-sampling principle inherent in NYFR, a computationally efficient method is introduced with compressive covariance sensing. That can be efficient implemented via only the non-uniform fast Fourier transform, fast Fourier transform, and some simple multiplication operations. Meanwhile, the state-of-the-art power spectrum reconstruction model for NYFR of time-domain and frequency-domain is constructed in this paper as a comparison. Furthermore, the computational complexity of the proposed method scales linearly with the Nyquist-rate sampled number of samples and the sparsity of spectrum occupancy. Simulation results and discussion demonstrate that the low complexity in sampling and computation is a more practical solution to meet the real-time wideband spectrum sensing applications

Distributed UAV Swarm Augmented Wideband Spectrum Sensing Using Nyquist Folding Receiver

Distributed unmanned aerial vehicle (UAV) swarms are formed by multiple UAVs with increased portability, higher levels of sensing capabilities, and more powerful autonomy. These features make them attractive for many recent applica-tions, potentially increasing the shortage of spectrum resources. In this paper, wideband spectrum sensing augmented technology is discussed for distributed UAV swarms to improve the utilization of spectrum. However, the sub-Nyquist sampling applied in existing schemes has high hardware complexity, power consumption, and low recovery efficiency for non-strictly sparse conditions. Thus, the Nyquist folding receiver (NYFR) is considered for the distributed UAV swarms, which can theoretically achieve full-band spectrum detection and reception using a single analog-to-digital converter (ADC) at low speed for all circuit components. There is a focus on the sensing model of two multichannel scenarios for the distributed UAV swarms, one with a complete functional receiver for the UAV swarm with RIS, and another with a decentralized UAV swarm equipped with a complete functional receiver for each UAV element. The key issue is to consider whether the application of RIS technology will bring advantages to spectrum sensing and the data fusion problem of decentralized UAV swarms based on the NYFR architecture. Therefore, the property for multiple pulse reconstruction is analyzed through the Gershgorin circle theorem, especially for very short pulses. Further, the block sparse recovery property is analyzed for wide bandwidth signals. The proposed technology can improve the processing capability for multiple signals and wide bandwidth signals while reducing interference from folded noise and subsampled harmonics. Experiment results show augmented spectrum sensing efficiency under non-strictly sparse conditions