•A novel hybrid modeling method is proposed for short-term wind speed forecasting.•Support vector regression model is constructed to formulate nonlinear state-space framework.•Unscented Kalman filter ...is adopted to recursively update states under random uncertainty.•The new SVR–UKF approach is compared to several conventional methods for short-term wind speed prediction.•The proposed method demonstrates higher prediction accuracy and reliability.
Accurate wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. Particularly, reliable short-term wind speed prediction can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, this task remains challenging due to the strong stochastic nature and dynamic uncertainty of wind speed. In this study, unscented Kalman filter (UKF) is integrated with support vector regression (SVR) based state-space model in order to precisely update the short-term estimation of wind speed sequence. In the proposed SVR–UKF approach, support vector regression is first employed to formulate a nonlinear state-space model and then unscented Kalman filter is adopted to perform dynamic state estimation recursively on wind sequence with stochastic uncertainty. The novel SVR–UKF method is compared with artificial neural networks (ANNs), SVR, autoregressive (AR) and autoregressive integrated with Kalman filter (AR-Kalman) approaches for predicting short-term wind speed sequences collected from three sites in Massachusetts, USA. The forecasting results indicate that the proposed method has much better performance in both one-step-ahead and multi-step-ahead wind speed predictions than the other approaches across all the locations.
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, as a globally prevalent evergreen tree, contains a wealth of bioactive components that play a crucial role in the pharmaceutical field.
extracts, defined as a collection of one or more bioactive ...compounds extracted from the genus
spp., have become a significant focus of modern cancer treatment research. This review article aims to delve into the scientific background of
extracts and their considerable value in pharmaceutical research. It meticulously sifts through and compares various advanced extraction techniques such as supercritical extraction, ultrasound extraction, microwave-assisted extraction, solid-phase extraction, high-pressure pulsed electric field extraction, and enzymatic extraction, assessing each technology's advantages and limitations across dimensions such as extraction efficiency, extraction purity, economic cost, operational time, and environmental impact, with comprehensive analysis results presented in table form. In the area of drug formulation design, this paper systematically discusses the development strategies for solid, liquid, and semi-solid dosage forms based on the unique physicochemical properties of
extracts, their intended medical uses, and specific release characteristics, delving deeply into the selection of excipients and the critical technical issues in the drug preparation process. Moreover, the article looks forward to the potential directions of
extracts in future research and medical applications, emphasizing the urgency and importance of continuously optimizing extraction methods and formulation design to enhance treatment efficacy, reduce production costs, and decrease environmental burdens. It provides a comprehensive set of preparation techniques and formulation optimization schemes for researchers in cancer treatment and other medical fields, promoting the application and development of
extracts in pharmaceutical sciences.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Optimizing wind power generation and controlling the operation of wind turbines to efficiently harness the renewable wind energy is a challenging task due to the intermittency and unpredictable ...nature of wind speed, which has significant influence on wind power production. A new approach for long-term wind speed forecasting is developed in this study by integrating GMCM (Gaussian mixture copula model) and localized GPR (Gaussian process regression). The time series of wind speed is first classified into multiple non-Gaussian components through the Gaussian mixture copula model and then Bayesian inference strategy is employed to incorporate the various non-Gaussian components using the posterior probabilities. Further, the localized Gaussian process regression models corresponding to different non-Gaussian components are built to characterize the stochastic uncertainty and non-stationary seasonality of the wind speed data. The various localized GPR models are integrated through the posterior probabilities as the weightings so that a global predictive model is developed for the prediction of wind speed. The proposed GMCM–GPR approach is demonstrated using wind speed data from various wind farm locations and compared against the GMCM-based ARIMA (auto-regressive integrated moving average) and SVR (support vector regression) methods. In contrast to GMCM–ARIMA and GMCM–SVR methods, the proposed GMCM–GPR model is able to well characterize the multi-seasonality and uncertainty of wind speed series for accurate long-term prediction.
•A novel predictive modeling method is proposed for long-term wind speed forecasting.•Gaussian mixture copula model is estimated to characterize the multi-seasonality.•Localized Gaussian process regression models can deal with the random uncertainty.•Multiple GPR models are integrated through Bayesian inference strategy.•The proposed approach shows higher prediction accuracy and reliability.
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Treatment of Klebsiella pneumoniae causing pyogenic infections is challenging. The clinical and molecular characteristics of
causing pyogenic infections are poorly understood, and antibacterial ...treatment strategies are limited. We analyzed the clinical and molecular characteristics of K. pneumoniae from patients with pyogenic infections and used time-kill assays to reveal the bactericidal kinetics of antimicrobial agents against hypervirulent K. pneumoniae (hvKp). A total of 54 K. pneumoniae isolates were included, comprising 33 hvKp and 21 classic K. pneumoniae (cKp) isolates, and the hvKp and cKp isolates were identified using five genes (
,
,
,
, and
) that have been applied as hvKp strain markers. The median age of all cases was 54 years (25th and 75th percentiles, 50.5 to 70), 62.96% of individuals had diabetes, and 22.22% of isolates were sourced from individuals without underlying disease. The ratios of white blood cells/procalcitonin and C-reactive protein/procalcitonin were potential clinical markers for the identification of suppurative infection caused by hvKp and cKp. The 54 K. pneumoniae isolates were classified into 8 sequence type 11 (ST11) and 46 non-ST11 strains. ST11 strains carrying multiple drug resistance genes have a multidrug resistance phenotype, while non-ST11 strains carrying only intrinsic resistance genes are generally susceptible to antibiotics. Bactericidal kinetics revealed that hvKp isolates were not easily killed by antimicrobials at susceptible breakpoint concentrations compared with cKp. Given the varied clinical and molecular features and the catastrophic pathogenicity of K. pneumoniae, it is critical to determine the characteristics of such isolates for optimal management and effective treatment of K. pneumoniae causing pyogenic infections.
Klebsiella pneumoniae may cause pyogenic infections, which are potentially life-threatening and bring great challenges for clinical management. However, the clinical and molecular characteristics of K. pneumoniae are poorly understood, and effective antibacterial treatment strategies are limited. We analyzed the clinical and molecular features of 54 isolates from patients with various pyogenic infections. We found that most patients with pyogenic infections had underlying diseases, such as diabetes. The ratio of white blood cells to procalcitonin and the ratio of C-reactive protein to procalcitonin were potential clinical markers for differentiating hypervirulent K. pneumoniae strains from classical K. pneumoniae strains that cause pyogenic infections. K. pneumoniae isolates of ST11 were generally more resistant to antibiotics than non-ST11 isolates. Most importantly, hypervirulent K. pneumoniae strains were more tolerant to antibiotics than classic K. pneumoniae isolates.
Purpose
The data protection is always a vital problem in the network era. High-speed cryptographic chip is an important part to ensure data security in information interaction. This paper aims to ...provide a new peripheral component interconnect express (PCIe) encryption card solution with high performance, high integration and low cost.
Design/methodology/approach
This work proposes a System on Chip architecture scheme of high-speed cryptographic chip for PCIe encryption card. It integrated CPU, direct memory access, the national and international cipher algorithm (data encryption standard/3 data encryption standard, Rivest–Shamir–Adleman, HASH, SM1, SM2, SM3, SM4, SM7), PCIe and other communication interfaces with advanced extensible interface-advanced high-performance bus three-level bus architecture.
Findings
This paper presents a high-speed cryptographic chip that integrates several high-speed parallel processing algorithm units. The test results of post-silicon sample shows that the high-speed cryptographic chip can achieve Gbps-level speed. That means only one single chip can fully meet the requirements of cryptographic operation performance for most cryptographic applications.
Practical implications
The typical application in this work is PCIe encryption card. Besides server’s applications, it can also be applied in terminal products such as high-definition video encryption, security gateway, secure routing, cloud terminal devices and industrial real-time monitoring system, which require high performance on data encryption.
Social implications
It can be well applied on many other fields such as power, banking, insurance, transportation and e-commerce.
Originality/value
Compared with the current strategy of high-speed encryption card, which mostly uses hardware field-programmable gate arrays or several low-speed algorithm chips through parallel processing in one printed circuit board, this work has provided a new PCIe encryption card solution with high performance, high integration and low cost only in one chip.
Wireless signal separation under the condition of multi-antenna and multi-channel reception is a process of estimating the source signal component by using the observed signal vector. Under the ...condition of multi-antenna and multi-channel reception of wireless mixed signals, in view of the problem that the stability of FastICA algorithm will be affected, it is proposed to increase the number of iterations to improve the FastICA algorithm. The experimental comparison and simulation results of FastICA algorithm and FastICA improved algorithm It shows that for the separation of wireless mixed signals, the improved FastICA algorithm can achieve a better separation effect.
Building models with limited data is one of the key steps towards the application of deep learning models in realistic scenarios. Under the framework of representation learning, we propose several ...novel algorithms for tackling challenging tasks in building models with limited data, including standard few-shot learning, incremental few-shot learning, and unsupervised few-shot learning. In the first part of this thesis, we propose an approach to learn low-rank representation that generalizes well to a new task using just a few training samples. High-quality representation can be found by averaging the weights of neural networks during the pre-training phase. Our approach achieves strong performance on both few-shot classification and regression benchmarks. We then consider incremental few-shot learning, in which the model incrementally learns new tasks from few-shot samples without forgetting old ones. We propose an approach to harmonize old knowledge preserving and new knowledge adaptation through quantized vectors of the learned representation. Prediction is made in a nonparametric way using similarity to learned reference vectors, which circumvents biased weights in a parametric classification layer during incremental few-shot learning. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods in incremental learning. In addition, we develop deep Laplacian eigenmaps to learn representation from unlabeled image data for downstream few-shot learning tasks. Our method learns representation by grouping similar images together and can be intuitively interpreted by random walks on augmented training data. The proposed method significantly closes the performance gap between supervised and unsupervised few-shot learning. To get us closer to general unsupervised representation learning across different data types, we present a domain-agnostic self-supervised learning method, which learns representation from unlabeled data without domain-specific data augmentations. The proposed method is adversarial perturbation based latent reconstruction (APLR), which is closely related to multi-dimensional Hirschfeld-Gebelein-Renyi maximal correlation and has theoretical guarantees on the linear probe error. APLR not only outperforms existing domain-agnostic self-supervised learning methods, but also closes the performance gap to domain-specific self-supervised learning methods on various domains, such as tabular data, images, and audio.
Batch processes are characterized by inherent nonlinearity, multiplicity of operating phases, between-phase transient dynamics and batch-to-batch uncertainty that pose significant challenges for ...accurate state estimation and quality prediction. Conventional multi-model strategies, however, may be ill-suited for multiphase batch processes because the localized models do not specially take into account the complex transient dynamics between two consecutive operating phases. In this study, a novel Bayesian model averaging based multi-kernel Gaussian process regression (BMA-MKGPR) approach is proposed for state estimation and quality prediction of nonlinear batch processes with multiple operating phases and between-phase transient dynamics. A kernel mixture model strategy is first used to identify the different operating phases of batch processes and then the multi-kernel GPR models are built for all the identified phases. Further, the between-phase transitional stage is determined by the posterior probabilities of measurement samples with respect to the two consecutive phases so that the Bayesian model averaging strategy can be designed to incorporate the two localized GPR models for handling the between-phase transient dynamics. For an arbitrary test sample within the transitional stage, its posterior probabilities with respect to the local models corresponding to the two consecutive phases are set as the adaptive weightings to integrate the corresponding local GPR models for state estimation and quality prediction. The proposed BMA-MKGPR approach is applied to a multiphase batch polymerization process and the result comparison demonstrates that the presented method can effectively handle multiple nonlinear operating phases, between-phase transient dynamics and process uncertainty with fairly high prediction accuracies.
► A novel nonlinear state estimation and quality prediction method is proposed. ► Multi-kernel Gaussian process regression models to deal with multiphase process. ► Bayesian model averaging to characterize between-phase transient dynamics. ► Local models are dynamically and adaptively integrated for transitional stages. ► The proposed method provides accurate and reliable state and quality estimations.
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In this document, a novel MIMO HARQ scheme is proposed, which jointly utilizes both chase combining and MLD. By utilizing the bit-wise soft-information (BWSI) derived from channel decoder iteratively ...for next retransmission's MLD effectively, the performances, such as PER and throughout seen from simulation results, outperforms conventional HARQ scheme.
Learning a new task from a handful of examples remains an open challenge in machine learning. Despite the recent progress in few-shot learning, most methods rely on supervised pretraining or ...meta-learning on labeled meta-training data and cannot be applied to the case where the pretraining data is unlabeled. In this study, we present an unsupervised few-shot learning method via deep Laplacian eigenmaps. Our method learns representation from unlabeled data by grouping similar samples together and can be intuitively interpreted by random walks on augmented training data. We analytically show how deep Laplacian eigenmaps avoid collapsed representation in unsupervised learning without explicit comparison between positive and negative samples. The proposed method significantly closes the performance gap between supervised and unsupervised few-shot learning. Our method also achieves comparable performance to current state-of-the-art self-supervised learning methods under linear evaluation protocol.
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