Redundant Factors Research Articles

Wheat Transcription Factor TaMYB60 Modulates Cuticular Wax Biosynthesis by Activating TaFATB and TaCER1 Expression.

Cuticular wax mixtures cover the epidermis of land plants and shield plant tissues from abiotic and biotic stresses. Although cuticular wax-associated traits are employed to improve the production of bread wheat, regulatory mechanisms underlying wheat cuticular wax biosynthesis remain poorly understood. In this research, partially redundant transcription factors TaMYB60-1 and TaMYB60-2 were identified as positive regulators of wheat cuticular wax biosynthesis. Knock-down of wheat TaMYB60-1 and TaMYB60-2 genes by virus-induced gene silencing resulted in attenuated wax accumulation and enhanced cuticle permeability. The roles of wheat fatty acyl-ACP thioesterase genes TaFATB1 and TaFATB2 in cuticular wax biosynthesis were characterized. Silencing wheat TaFATB1 and TaFATB2 genes led to reduced wax accumulation and increased cuticle permeability, suggesting that TaFATB1 and TaFATB2 genes positively contribute to wheat cuticular wax biosynthesis. Importantly, transcription factors TaMYB60-1 and TaMYB60-2 exhibit transcriptional activation ability and could stimulate the expression of wax biosynthesis genes TaFATB1, TaFATB2, and ECERIFERUM 1 (TaCER1). These findings support that transcription factor TaMYB60 positively regulates wheat cuticular wax biosynthesis probably by activating transcription of TaFATB1, TaFATB2, and TaCER1 genes.

Advanced regression model fitting to experimental results in case of the effect of grinding conditions on a cutting force with the planer technical knives

The use of well-fit models enables users to make better decisions and draw accurate insights, so in this article a multi-step procedure for establishing regression functions for experimental data is presented. This was inspired by the need to determine well-fitted models for the results of the study of the relationship between the input factors of the grinding process of planer knives and the measured grinding power, as well as in relation to the knife cutting force. The sharpening process was carried out under various conditions, making it possible to assess their influence on the results and select the most favorable technological parameters to prepare the knives for operation. The equations adjusted by Excel or other basic methods require additional verification if the mathematical formula and values of the model coefficients correspond to the data and the physical meaning. For this purpose, one-, two- and three-factor analyses were performed to eliminate redundant factors and introduce significant interactions between input parameters. Additional analyses of variance and Tukey, Levene and Brown-Forsythe tests were used to determine the general form of the model. Detailed form of nonlinear models was worked out by comparing the distribution of residuals, looking for outliers and evidence of the presence or absence of collinearity, as well as by determining the values of additional model quality indicators, such as Akaike Information Criterion, Cook’s distance and Variance Inflation Factor. The models presented include a categorization variable (grinding type) and are presented graphically in the form of surface plots. With reference to the technological processes studied, a discussion of the physical significance of the developed models and the influence of the input factors on the model output value was conducted. The analyzes additionally include an evaluation of the model factors using prediction slice plots.

The Relative Importance of Cytotoxins Produced by Methicillin-Resistant Staphylococcus aureus Strain USA300 for Causing Human PMN Destruction.

Staphylococcus aureus (S. aureus) is a prominent Gram-positive bacterial pathogen that expresses numerous cytotoxins known to target human polymorphonuclear leukocytes (PMNs or neutrophils). These include leukocidin G/H (LukGH, also known as LukAB), the Panton-Valentine leukocidin (PVL), γ-hemolysin A/B (HlgAB), γ-hemolysin B/C (HlgBC), leukocidin E/D (LukED), α-hemolysin (Hla), and the phenol-soluble modulin-α peptides (PSMα). However, the relative contribution of each of these cytotoxins in causing human PMN lysis is not clear. In this study, we used a library of cytotoxin deletion mutants in the clinically relevant methicillin-resistant S. aureus (MRSA) isolate LAC (strain ST8:USA300) to determine the relative importance of each for causing human PMN lysis upon exposure to extracellular components as well as following phagocytosis. Using flow cytometry to examine plasma membrane permeability and assays quantifying lactose dehydrogenase release, we found that PVL was the dominant extracellular factor causing human PMN lysis produced by USA300. In contrast, LukGH was the most important cytotoxin causing human PMN lysis immediately following phagocytosis with contributions from the other bicomponent leukocidins only observed at later time points. These results not only clarify the relative importance of different USA300 cytotoxins for causing human PMN destruction but also demonstrate how two apparently redundant virulence factors play distinctive roles in promoting S. aureus pathogenesis.

Increasing the period of active use of on-board electronic equipment of spacecraft

For electronic equipment of space systems, and primarily memory devices, the task of protection from the effects of ionizing cosmic radiation and other external factors that distort stored and processed information is relevant. This paper proposes a holographic coding method that allows you to restore information in the event of a large number of errors. The method is based on recording into memory, instead of the original digital hologram data, a virtual digital object corresponding to a data block. The divisibility property of a hologram is used, which makes it possible to reconstruct a recorded data block from its fragment. The achieved level of noise immunity is determined by the size of the hologram. For an 8-bit data block, recording a 256-bit hologram provides information recovery if 75 % of the recorded hologram is lost. The developed decoder corrects a package of dependent (grouping) errors that distort all bits of the hologram. The number of random independent errors that the decoder corrects can be up to 40 % of the recorded information. The information storage system, resistant to ionizing radiation, is a memory array of increased capacity, taking into account the selected redundancy factor, and a memory controller that performs holographic encoding when recording information and decoding with automatic error correction when reading information. The operating algorithm of the controller itself can be implemented in the form of a programmable logic integrated circuit, or stored in a read-only memory device that is not affected by ionizing radiation.

Enhancing Seismic Landslide Susceptibility Analysis for Sustainable Disaster Risk Management through Machine Learning

The accuracy of Seismic Landslide Susceptibility Maps (SLSMs) is imperative for the prevention of seismic landslide disasters. This study enhances the precision of SLSMs by integrating nine distinct machine learning methodologies with the GeoDetector version 0.0.4 tool to filter both numerical and physical factors contributing to landslide susceptibility. The dataset comprises 2317 landslide instances triggered by the 2013 Minxian Ms = 6.6 earthquake, from which redundant factors were pruned using the Recursive Feature Elimination technique. Subsequent evaluations of the optimized factors, both individually and in combination, were conducted through Frequency Ratio analysis and Factor Interaction assessment. The study juxtaposes the Area Under the Receiver Operating Characteristic Curve (AUC) and the accuracy of nine machine learning models before and after factor optimization. The findings indicate an increase in AUC from a maximum of 0.989 to 0.992 in the Random Forest model, and an 8.37% increase in AUC for the SVM model, signifying a notable enhancement in the stability across all models. The establishment of the SLSM notably elevated the frequency ratio in high-risk zones from 50.40 to 85.14, underscoring the efficacy of combining machine learning and detector optimization techniques in sustainable practices. This research proposes a universal framework aimed at eliminating redundancy and noise in SLSMs and hazard risk assessments, thereby facilitating sustainable geological disaster risk management.

The Fama-French Three Factor Model Test on Excess Stock Return: Evidence From Hong Kong, Indonesia and Singapore Capital Market

The presence of the capital market has a very important role in the world economy because the capital market carries out economic and financial functions. One of the instruments in the capital market is stock. In stock investment, investors expect a return with a size that is in accordance with the level of risk they can afford. This study wants to test whether the Fama-French Three Factor Model variables, namely market excess returns, book-to-market equity and firm size, can be used to explain excess returns on the stock markets of Hong Kong and Indonesia with the observation period of 2018–2022. Based on the test results multiple linear regression, Gibbons, Ross, and Shanken test (GRS test) and spanning test, this study found that the variables in the Fama-French Three Factor Model can simultaneously be used to explain the excess return on all stock portfolios on the stock market in Hong Kong and Indonesia, and can only be used to explain the excess return on Big and S-H portfolios in Singapore. Based on the results of the GRS-test, in the Hong Kong market the most efficient portfolio is S-M, in the Indonesian market the most efficient portfolio is the Big portfolio, while in the Singapore market all portfolios are efficient and based on the spanning test, in the Hong Kong market the potential redundant factor is SMB, in in the Indonesian market, the potential redundant factor is HML, while in the Singapore market, there is no redundant potential factor.

Two redundant transcription factor binding sites in a single enhancer are essential for mammalian sex determination.

Male development in mammals depends on the activity of the two SOX gene: Sry and Sox9, in the embryonic testis. As deletion of Enhancer 13 (Enh13) of the Sox9 gene results in XY male-to-female sex reversal, we explored the critical elements necessary for its function and hence, for testis and male development. Here, we demonstrate that while microdeletions of individual transcription factor binding sites (TFBS) in Enh13 lead to normal testicular development, combined microdeletions of just two SRY/SOX binding motifs can alone fully abolish Enh13 activity leading to XY male-to-female sex reversal. This suggests that for proper male development to occur, these few nucleotides of non-coding DNA must be intact. Interestingly, we show that depending on the nature of these TFBS mutations, dramatically different phenotypic outcomes can occur, providing a molecular explanation for the distinct clinical outcomes observed in patients harboring different variants in the same enhancer.

Predicting soil erosion and sediment delivery in large, data‐sparse, mountainous basins

AbstractIntense hydroelectric exploitation requires information on sediment flux to prevent the potential siltation of cascade reservoirs. As the main sediment source for the Yangtze River, the lower Jinsha River Basin (LJRB) lacks data on its sediment budget, which urges a thorough investigation to provide reliable estimates. This paper proposed a modelling framework to study soil erosion, sediment delivery, and sediment yield in this critical area with an emphasis on analyzing the underlying controls. Over the past 50 years, basin average erosion rates decreased from 26.5 to 23.7 t ha−1 a−1, with erosion hotspots (>50 t ha−1 a−1) comprising only 21.1% of the basin area but contributing 69.5% of the gross erosion. Local steepness and sparse landcover conditions were found to be directly responsible for high erosions, rather than precipitation or soils. With a dimensionality reduction strategy to remove redundant factors, the optimal variables for modelling sediment delivery ratio (SDR) were identified as Specific Catchment Area, Maximum Elevation, and Drainage Area. These three variables all had a positive correlation with SDR, and the established model can explain 86% of the SDR variation. Further modelling revealed regionalised SDRs ranging from 0.07 to 0.38 for 39 subbasins, about 30% of which were no less than 0.35. Regionalised specific sediment yield (SSY) ranged from 1.3 to 16.9 t ha−1 a−1 with 38% of the high SSYs (above 8 t ha−1 a−1) concentrating in the reservoir area from Wudongde to Xiluodu that poses siltation risks. This study addressed the scarcity of sediment budget knowledge in the LJRB, and the coupled model of erosion and sediment delivery has been validated with satisfactory predictions. Therefore, the modelling framework proposed may be adopted to diverse river basins for its low parameter requirements and solid modelling performance.

Prognosis of comatose patients with reduced EEG montage by combining quantitative EEG features in various domains.

As the frontoparietal network underlies recovery from coma, a limited frontoparietal montage was used, and the prognostic values of EEG features for comatose patients were assessed. Collected with a limited frontoparietal EEG montage, continuous EEG recordings of 81 comatose patients in ICU were used retrospectively. By the 60-day Glasgow outcome scale (GOS), the patients were dichotomized into favorable and unfavorable outcome groups. Temporal-, frequency-, and spatial-domain features were automatically extracted for comparison. Partial correlation analysis was applied to eliminate redundant factors, and multiple correspondence analysis was used to explore discrimination between groups. Prognostic characteristics were calculated to assess the performance of EEG feature-based predictors established by logistic regression. Analyses were performed on all-patients group, strokes subgroup, and traumatic brain injury (TBI) subgroup. By analysis of all patients, raised burst suppression ratio (BSR), suppressed root mean square (RMS), raised power ratio of β to α rhythm (β/α), and suppressed phase-lag index between F3 and P4 (PLI [F3, P4]) were associated with unfavorable outcome, and yielded AUC of 0.790, 0.811, 0.722, and 0.844, respectively. For the strokes subgroup, the significant variables were BSR, RMS, θ/total, θ/δ, and PLI (F3, P4), while for the TBI subgroup, only PLI (F3, P4) was significant. BSR combined with PLI (F3, P4) gave the best predictor by cross-validation analysis in the all-patients group (AUC = 0.889, 95% CI: 0.819-0.960). Features extracted from limited frontoparietal montage EEG served as valuable coma prognostic tools, where PLI (F3, P4) was always significant. Combining PLI (F3, P4) with features in other domains may achieve better performance. A limited-montage EEG coupled with an automated algorithm is valuable for coma prognosis.

An Analysis of the Applicability of Pricing Models in the Chinese Stock Market

The stock market in China has an important position in the world, but it has not been able to find a pricing model suitable for the market. This paper reviews the history and development of various pricing models, analyzes relevant research on their application in the Chinese stock market, and discusses the applicability of various pricing models. It is found that the applicability of the capital asset pricing model (CAPM) in China is low; the applicability of the Fama-French three-factor model and the Fama-French five-factor model is better than CAPM, but there is significant controversy over the advantages and disadvantages of the two. It is also found that the applicability of the five-factor model may have regional differences in different markets, and there may be differences in redundant factors in different markets. It is proposed that future research should obtain more accurate market data, explore new influencing factors, and build a pricing model more suitable for the stock market in China by combining the characteristics based on more advanced data analysis technology.

Abstract A126: Characterization of proteasome stress response reporter mammalian cell lines for the discovery of DDI2 inhibitors in multiple myeloma

Abstract Background The goal of this study was to develop and characterize proteasome stress response (PSR) reporter cell lines to aid in the identification of inhibitors of the aspartic protease DDI2. We recently showed that multiple myeloma (MM) is dependent on DDI2 for survival due to its critical role in activating PSR master-regulator NRF1. Cleaved NRF1 induces de novo proteasome subunit transcription in face of proteasome insufficiency, thus maintaining proteostasis and mediating resistance to proteasome inhibitors (PI), a mainstay of MM therapy. We hypothesize that targeting DDI2 represent an unexplored, attractive therapeutic strategy in MM, able to overcome acquired and innate resistant to PI, a major clinical hurdle in the care of MM patients. We further hypothesize that due to intrinsic proteostasis vulnerability in MM, DDI2 inhibitors will have a favorable therapeutic index in the clinics. However, specific DDI2 inhibitors are not available and mammalian reporter cell lines of PSR activation do not exist, limiting our capabilities to successfully screen for new drugs. Methods We generated a reporter construct comprising 8 repeats of NRF1 DNA binding site, antioxidant response elements (ARE), upstream of a minimal promoter followed by destabilized GFP. To monitor for successful transfection, copy number integration and global changes in transcription, we cloned mCherry under an EF1α promotor within the same vector. We used this lentiviral construct to transduce MM cell lines characterized by distinct sensitivity to PIs, including AMO1-VR, as a model of acquired bortezomib resistance, and DDI2 KO AMO1-VR to assess for DDI2-independent PSR suppression. To induce PSR, we pulse treated cells with a sublethal dose of carfilzomib and monitored changes in the ratio of GFP/mCherry intensity over time via flow cytometry and immunofluorescence. Results We found that the reporter cell lines showed a peak of GFP/mCherry intensity 8-12 hours after carfilzomib treatment, consistent with our prior data showing peak of proteasome subunit transcription at 10 hours. Importantly, the GFP/mCherry ratio returned to baseline after approximately 24 hours from treatment, in the absence of significant cell apoptosis. DDI2 KO AMO1-VR showed significantly blunted peak of GFP/mCherry intensity, consistent with reduced capability to induce proteasome subunit transcription. We hypothesize that redundant transcription factors, such as NRF2 are responsible for the blunted PSR observed, consistent with increased expression of NRF2 in DDI2 KO cells. Importantly, treatment with sublethal doses of genetoxic drug doxorubicin did not induce significant activation of reporter construct. Conclusions We have successfully developed the first mammalian reporter cell line of PSR with preliminary data showing adequate delta and kinetics to assist in pharmacological screening of molecules blocking the PSR and inducing apoptosis in MM. This powerful tool will inform screening and subsequent validation of newly found/synthesized DDI2 inhibitors, currently under development. Citation Format: Cameron VanCleave, Tianzeng Chen, Matthew Ho, Maria Moscvin, Peter G Czarnecki, Giada Bianchi. Characterization of proteasome stress response reporter mammalian cell lines for the discovery of DDI2 inhibitors in multiple myeloma [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A126.

Negligible role for pneumococcal surface protein A (PspA) and pneumococcal surface protein C (PspC) in the nasopharyngeal colonization of mice with a serotype 6B pneumococcal strain

Streptococcus pneumoniae colonizes the human nasopharynx asymptomatically, but it can also cause several diseases, including otitis media, pneumonia, bacteremia, and meningitis. The colonization of the nasopharynx by the bacteria is an essential step for the pneumococcus to invade other sites and cause diseases. Pneumococcal surface protein A (PspA) and Pneumococcal surface Protein C (PspC) are important virulence factors and have been described to play roles in adhesion and immune evasion. In this study, we immunized mice subcutaneously with the recombinant α-helical region of PspA and/or PspC combined with different adjuvants to assess protection against colonization with the serotype 6B strain BHN418. Though high serum levels of specific IgG were detected, none of the formulations led to reduction in the colonization of the nasopharynx. The negative result may be due to the poor induction of IgG2c, which has been previously correlated with protection against pneumococcal colonization in mice. Furthermore, BHN418 pspA and pspC single and double knockouts were evaluated in colonization experiments and no differences in bacterial load were observed. In competition assays with the wild-type strain, borderline to no reduction was observed in the loads of the knockouts. Our results contrast with data from the literature using other pneumococcal strains, showing that the role of PspA and PspC in colonization can vary depending on the background of the knockout strain studied. BHN418 has been selected for its capacity to colonize humans in experimental challenge studies and may have redundant factors that compensate for the lack of PspA and PspC during nasopharyngeal colonization of mice.

An Influenza A virus can evolve to use human ANP32E through altering polymerase dimerization

Human ANP32A and ANP32B are essential but redundant host factors for influenza virus genome replication. While most influenza viruses cannot replicate in edited human cells lacking both ANP32A and ANP32B, some strains exhibit limited growth. Here, we experimentally evolve such an influenza A virus in these edited cells and unexpectedly, after 2 passages, we observe robust viral growth. We find two mutations in different subunits of the influenza polymerase that enable the mutant virus to use a novel host factor, ANP32E, an alternative family member, which is unable to support the wild type polymerase. Both mutations reside in the symmetric dimer interface between two polymerase complexes and reduce polymerase dimerization. These mutations have previously been identified as adapting influenza viruses to mice. Indeed, the evolved virus gains the ability to use suboptimal mouse ANP32 proteins and becomes more virulent in mice. We identify further mutations in the symmetric dimer interface which we predict allow influenza to adapt to use suboptimal ANP32 proteins through a similar mechanism. Overall, our results suggest a balance between asymmetric and symmetric dimers of influenza virus polymerase that is influenced by the interaction between polymerase and ANP32 host proteins.

Indoor localization for an unknown radio emitter employing graph-based optimization and improved RSSD

Accurately locating an indoor unknown radio emitter (URE) is a challenging target to ensure telecommunication security. The URE positioning method based on received signal strength difference (RSSD) has attracted considerable attention due to the advantage of not being affected by transmit power and frequency. However, the RSSD-based fingerprint technique cannot accurately express the constraint equations between signal characteristics and geographic coordinates because of redundant databases and false matching. In this paper, a novel RSSD-based indoor positioning method using factor graph (FG) for an URE is proposed to improve positioning accuracy and reduce computational complexity. Firstly, the databases are reconstructed by singular value decomposition (SVD) to eliminate redundant factor nodes. Secondly, Pearson correlation coefficient (PCC) is utilized to determine the sub-positioning area. Combing SVD with PCC, the hyperplane equations are reconstructed to build an optimized FG model, called SP-FG. Considering simulation and experiment, the proposed SP-FG algorithm improves the cumulative distribution function (CDF) of average positioning error within 0.5 m by 10% and 14% compared with conventional FG algorithm and K-nearest neighbor (KNN) algorithm, respectively. In addition, this paper discusses the superiority of proposed SP-FG algorithm in positioning accuracy under different reference side lengths, access point (AP) coordinates and numbers.

Coarse Aggregate Shape Classification Method Based on Per-Optuna-LightGBM Model

To improve the detection level of aggregate shape for automated road use, Per-Optuna-LightGBM model for aggregate shape classification is proposed. Collect aggregate images using industrial camera and extract 48 morphological feature parameters. A feature importance analysis method based on Spearman Correlation and Permutation Importance is proposed to remove redundant factors and select the feature parameters of aggregate morphology. Based on cross-validation, an optimized Optuna-LightGBM model is trained based on the constructed dataset. Compared with GS-XGBoost algorithm, the Optuna-LightGBM model can classify the shape of aggregates more accurately and efficiently. The accuracy value of the proposed model is 82.5%, which increased by 4% compared to before optimization. The proposed model can efficiently classify the shape of aggregates which meet the design requirements, also provide a certain foundation for automated classification of aggregate shapes.

Multiple discrete orthonormal S-transforms and its application in analyzing, modelling, and simulating random process and field

We examine the derivation and characteristics of the basis function employed in the discrete orthonormal S-transform (DOST). The examination is aimed at clarifying the interpretation of the power characteristics associated with the partitions and basis functions employed in DOST. Based on this examination, it is suggested that a one-half shift to the time index of the original DOST basis functions could be considered to facilitate the interpretation of the power distribution in the time–frequency domain. Although the non-redundant DOST is a highly efficient transform, its resolution in the time–frequency space is relatively coarse as compared to some of the continuous time–frequency decomposition techniques. To improve the resolution, and without significantly increasing the computational time, we propose the multiple DOSTs (MDOSTs) approach that uses M DOSTs, each associated with a different partition scheme in the time–frequency domain and a set of corresponding orthogonal basis functions. The redundancy factor of MDOSTs is simply equal to M. In addition, we show that the MDOSTs can be used to model and simulate nonstationary non-Gaussian processes and fields within the framework of the iterative power and amplitude correction algorithm. The use of MDOSTs for analyzing, modelling, and simulating seismic ground motion and corrosion surface is illustrated by numerical examples. The results show that using MDOSTs can effectively improve the time–frequency resolutions while maintaining high computational efficiency.

A Small Compound, HYGIC, Promotes Hypocotyl Growth Through Ectopic Ethylene Response.

Plant seedlings adjust the growth of the hypocotyl in response to surrounding environmental changes. Genetic studies have revealed key players and pathways in hypocotyl growth, such as phytohormones and light signaling. However, because of genetic redundancy in the genome, it is expected that not-yet-revealed mechanisms can be elucidated through approaches different from genetic ones. Here, we identified a small compound, HYGIC (HG), that simultaneously induces hypocotyl elongation and thickening, accompanied by increased nuclear size and enlargement of cortex cells. HG-induced hypocotyl growth required the ethylene signaling pathway activated by endogenous ethylene, involving CONSTITUTIVE PHOTOMORPHOGENIC 1, ETHYLENE INSENSITIVE 2 (EIN2) and redundant transcription factors for ethylene responses, ETHYLENE INSENSITIVE 3 (EIN3) and EIN3 LIKE 1. By using EBS:GUS, a transcriptional reporter of ethylene responses based on an EIN3-binding-cis-element, we found that HG treatment ectopically activates ethylene responses at the epidermis and cortex of the hypocotyl. RNA-seq and subsequent gene ontology analysis revealed that a significant number of HG-induced genes are related to responses to hypoxia. Indeed, submergence, a representative environment where the hypoxia response is induced in nature, promoted ethylene-signaling-dependent hypocotyl elongation and thickening accompanied by ethylene responses at the epidermis and cortex, which resembled the HG treatment. Collectively, the identification and analysis of HG revealed that ectopic responsiveness to ethylene promotes hypocotyl growth, and this mechanism is activated under submergence.

Imaging feature-based clustering of financial time series.

Timeseries representation underpin our ability to understand and predict the change of natural system. Series are often predicated on our choice of highly redundant factors, and in fact, the system is driven by a much smaller set of latent intrinsic keys. It means that a better representation of data makes points in phase space clearly for researchers. Specially, a 2D structure of timeseries could combine the trend and correlation characters of different periods in timeseries together, which provides more clear information for top tasks. In this work, the effectiveness of 2D structure of timeseries is investigated in clustering tasks. There are 4 kinds of methods that the Recurrent Plot (RP), the Gramian Angular Summation Field (GASF), the Gramian Angular Differential Field (GADF) and the Markov Transition Field (MTF) have been adopted in the analysis. By classifying the CSI300 and S&P500 indexes, we found that the RP imaging series are valid in recognizing abnormal fluctuations of financial timeseries, as the silhouette values of clusters are over 0.6 to 1. Compared with segment methods, the 2D models have the lowest instability value of 0. It verifies that the SIFT features of RP images take advantage of the volatility of financial series for clustering tasks.

Carbon price prediction based on LsOALEO feature selection and time-delay least angle regression

Accurate carbon price prediction can help governments to improve the environment and reduce carbon emissions at a lower cost. To improve the prediction accuracy of carbon price, many scholars have attempted to use multiple factors to predict the carbon price. This method improves carbon price prediction, but also brings problems such as increased complexity and decreased interpretability of prediction models. When analyzing the influencing factors of carbon price, scholars tend to overlook the coupling relationship between feature selection and time delay, resulting in redundant or missing influencing factors. To solve the above problems, we innovatively propose a carbon price prediction model based on LsOALEO feature selection and time-delay least angle regression (LsOALEO-FSTDLARS). The model is mainly composed of two parts: (1) Based on LARS, the prediction model is constructed by integrating feature selection and time delay, and the feasibility of the model is proved theoretically. (2) On the basis of lioness optimization algorithm, the local escape operator is used to improve the global search ability of lioness optimization algorithm, and the algorithm is used for feature selection and time delay optimization. Finally, we conduct experiments on the data sets of the carbon markets in Guangdong, Shanghai, Hubei, Tianjin and China, and compares the proposed method with various other prediction methods. The results of empirical analysis show that compared with the best performing single and combined forecasting methods, LsOALEO-FSTDLARS has an average reduction of 31.05% and 1.30% in RMSE, 46.62% and 5.37% in MAPE, and an average increase of 31.39% and 0.94% in R2 respectively. All of the above indicate that the LsOALEO-FSTDLARS model has higher prediction accuracy, stronger generalization ability and more stable effect, and can be used as an effective tool for carbon price prediction.

High Efficiency Converters Based on Modular Partial Power Processing for Fully Electric Maritime Applications

This paper proposes an approach for analyzing the benefits that partial-power-processing-based converters can bring to fully electric maritime applications. With the aim of making the system modular and scalable to different powers/energies, series-connected partial power converters are proposed. Serializing these converters entails significant overvoltage issues, and this paper tackles them for one series-connected module failure case. A reliability analysis has been carried out considering that the components of the battery system follow an independent and identical distribution in terms of failure probability. Furthermore, a redundancy factor has been added to allow a certain failure rate in what is known as a fault-tolerant system. Finally, to demonstrate the high efficiency of partial power converters, a 3 kW prototype is tested at different working points that model the charging process of a battery. The experimental results show a peak efficiency of 99.36%.