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As one of the effective renewable energy sources, wind energy has received attention because it is sustainable energy. Accurate wind speed forecasting can pave the way to the goal of sustainable development. However, current methods ignore the temporal characteristics of wind speed, which leads to inaccurate forecasting results. In this paper, we propose a novel SSA-CCN-ATT model to forecast the wind speed. Specifically, singular spectrum analysis (SSA) is first applied to decompose the original wind speed into several sub-signals. Secondly, we build a new deep learning CNN-ATT model that combines causal convolutional network (CNN) and attention mechanism (ATT). The causal convolutional network is used to extract the information in the wind speed time series. After that, the attention mechanism is employed to focus on the important information. Finally, a fully connected neural network layer is employed to get wind speed forecasting results. Three experiments on four datasets show that the proposed model performs better than other comparative models. Compared with different comparative models, the maximum improvement percentages of MAPE reaches up to 26.279%, and the minimum is 5.7210%. Moreover, a wind energy conversion curve was established by simulating historical wind speed data. [ABSTRACT FROM AUTHOR]

Hepatic fibrosis is a reversible pathological phenomenon, in the early and middle stages, but but no satisfactory intervention drugs have been available so far. Recent studies have suggested that microcirculation disturbance of liver is one of the important pathogenesis of chronic liver disease, the improvement of microcirculation is beneficial to the recovery of liver function and the delay of liver fibrosis. Hepatic stellate cells are the core cells of hepatic fibrosis, and also the most critical cells that affect the microcirculation of the liver. While TLR4/MyD88/NF-K.B and TLR4/MyD88/MAPKs which are based on the action of hepatic stellate cells are two pathways that have very important influence on the inflammatory response of liver, the proliferation and apoptosis of hepatic stellate cells, and the secretion of fibrogenic cytokines. It was found that Plumbapin, the active ingredient of Guangxi specialty ethnic medicine, has the definite effect of promoting blood circulation and removing blood stasis and anti-hepatic fibrosis, but its mechanism is not clear. In this study, the research progress of the above problems was reviewed, and further research ideas were derived as follows: the pharmacological effect of Plumbapin on anti-hepatic fibrosis, promoting blood circulation and removing stasis was based on the influence of TLR4/MyD88/NF-K.B and MAPKs signal pathway. [ABSTRACT FROM AUTHOR]

Hepatic sinusoidal endothelial cells (HSEC) are the most important cells that constitute the microcirculation barrier of liver. Clinically, W-P bodies have been detected in the HSEC cells of most patients with liver fibrosis, and these bodies have become the synthesis and storage sites of vW factor, ET-1 and other factors of liver fibrosis; during pathological stimulation, W-P bodies will excrete the above factors into the cytoplasm, which can make the structure and function of HSEC maladjusted, cause the disturbance of liver microcirculation, and aggravate the process of liver fibrosis. However, previous studies have found that Plumbagin, the active ingredient of Guangxi specialty ethnic medicine, has the definite function of promoting blood circulation and removing blood stasis and anti-liver fibrosis, but its mechanism is not clear. In this study, the research progress of the above problems was reviewed, and further research ideas were derived from it as follows: the role of Plumbagin in promoting blood circulation and removing blood stasis and anti-liver fibrosis is produced by affecting the formation quantity of W-P bodies, affecting the synthesis and storage of the contents of W-P bodies, and interfering with their exocytosis ability. [ABSTRACT FROM AUTHOR]

Particulate Matters such as PM10, PM2.5 may contain heavy metal oxides and harmful substances that threaten human health and environmental quality. In this paper, we propose a new combined neural network algorithm which based on Elman, echo state network (ESN) and cascaded BP neural network (CBP) to predict PM10 and PM2.5. In order to further improve the performance of the prediction result, we use the simulated annealing algorithm (SA) to optimize the parameters in the combination method to form the optimal combination model. And particle swarm optimization (PSO) is used to optimize the parameters in ESN. The chemical species in the atmosphere which include SO2, NO, NO2, O3 and CO in Baiyin, Gansu Province of China are used to test and verify the proposed combined method. The experimental results show that the prediction performance of the combined model presented in this paper is indeed superior to other three neural network models. [ABSTRACT FROM AUTHOR]

Network traffic prediction performs a main function in characterizing network community performance. An approach which could appropriately seize the salient characteristics of the network visitors could be very useful for network analysis and simulation. Network traffic prediction methods could be divided into two classes: one is the single models and the opposite is the hybrid fashions. The hybrid models integrate the merits of several single models and consequently can enhance the network traffic prediction accuracy. In this paper, a new hybrid network traffic prediction method (EPSVM) primarily based on Empirical Mode Decomposition (EMD), Particle Swarm Optimization (PSO), and Support Vector Machines (SVM) is presented. The EPSVM first utilizes EMD to eliminate the impact of noise signals. Then, SVM is applied to model training and fitting, and the parameters of SVM are optimized by PSO. The effectiveness of the presented method is examined by evaluating it with different methods, including basic SVM (BSVM), Empirical Mode Decomposition processed by SVM (ESVM), and SVM optimized by Particle Swarm Optimization (PSVM). Case studies have demonstrated that EPSVM performed better than the other three network traffic prediction models. [ABSTRACT FROM AUTHOR]

The load balancing technology is widely used in current enterprise network to provide high quality and reliable service. Conventional load balancing technology is often achieved by specific hardware that is usually very expensive and lacks sufficient flexibility. Meanwhile, it is easy to become a single point of failure and would be restricted in virtualization environments. Thus, we propose a load balancing algorithm based on server running state, which can calculate comprehensive loading according to the CPU utilization, memory utilization, and network traffic of the servers. Furthermore, a load balancing solution based on software defined networks (SDN) technology is applied in this paper, and it is designed and implemented in OpenFlow network. We combine network management and server state monitor in this scheme, in which the OpenFlow switches forward the request to the least comprehensive loading server by modifying the packet. [ABSTRACT FROM AUTHOR]

A fuel cell is an electrochemical device that converts the chemical energy of a fuel and oxidant into electricity. Cation-exchange and anion-exchange membranes play an important role in hydrogen fed proton-exchange membrane (PEM) and anion-exchange membrane (AEM) fuel cells, respectively. Over the past 10 years, there has been growing interest in using nanofiber electrospinning to fabricate fuel cell PEMs and AEMs with improved properties, e.g., a high ion conductivity with low in-plane water swelling and good mechanical strength under wet and dry conditions. Electrospinning is used to create either reinforcing scaffolds that can be pore-filled with an ionomer or precursor mats of interwoven ionomer and reinforcing polymers, which after suitable processing (densification) form a functional membrane. In this review paper, methods of nanofiber composite PEMs and AEMs fabrication are reviewed and the properties of these membranes are discussed and contrasted with the properties of fuel cell membranes prepared using conventional methods. The information and discussions contained herein are intended to provide inspiration for the design of high-performance next-generation fuel cell ion-exchange membranes. [ABSTRACT FROM AUTHOR]

As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability. [ABSTRACT FROM AUTHOR]

Model-mediated teleoperation has been developed to improve both transparency and stability in teleoperation. It uses local model of remote environment to provide non-delayed force feedback rather than using delayed force signals from slave side and thus is robust to arbitrary time delay. However, updating parameters in the local model may cause sudden force change during the operation. Meanwhile, the undesirable deep penetration or overlarge contact force may occur on the slave side due to the modeling error. Both of them will jeopardize the system stability. In this article, we propose a novel force-based model updating algorithm, which restrains the abrupt force caused by parameter updating. The update efficiency has been greatly improved by comparing with the existing solution; meanwhile, it ensures a stable human–machine interaction at the same time. Then, a new adaptive impedance controller that restricts both overlarge force and penetration is introduced. The obtained results on a one-degree of freedom contact experiment with a delay of 5 s demonstrate the superiority of proposed approaches in comparison with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Schematic synthetic procedure of VES-Ir prodrug and construction of self-assembled VES-Ir nanoparticles (VES-Ir NPs), co-delivery and low pH and esterase release and synergistic antitumor effect after endo -lysomal internalization into tumor cell. Irinotecan (Ir) is a potent antitumor chemotherapeutics in clinic and used for the treatment of a various cancers, but the degree of its application is critically limited by toxic side-effects and marked heterogeneities. Nano-formulation of prodrugs, based on "all-in-one" carrier-free self-assemblies offers an effective approach to alter pharmacokinetics and safety profiles of cytotoxic agents. In this study, a novel vitamin E succinate-based formulation of Ir (VES-Ir) combined with nanoscaled characteristics and synergistic combination was constructed through esterification. The conjugation makes amphiphilic VES-Ir prodrug self-assemble into nanoparticles with a fine diameter (VES-Ir NPs, 75.4 nm) of spherical morphology. Furthermore, VES-Ir NPs with a 1:1 drug-to-drug ratio was demonstrated to possess respectable physiological stability within 72 h test, while can react to pH/esterase-sensitive drug release in lysosomes internalized into tumor cells, potentially highlighting their alleviating side effects. Compared with single and mixture drugs administration, the nanoformulated VES-Ir NPs codelivered both VES and Ir with different anticancer mechanisms to induce the highest suppress proliferation of MCF-7 (IC 50 0.18 μM) and A549 (IC 50 0.29 μM) cells in a synergistic way (CI < 1). More importantly, the formulating nanoparticulate Ir is to significantly enhance its bioavailability in vivo with long retention time in bloodstream and thereby, resulting the superior tumor inhibitory rate (TIR) of 85.2% versus controls. This simple nanoformulation of Ir drug deprived from VES conjugation, together with self-delivery and synergistic property, may provide an effective strategy for multiple chemotherapeutics delivery to treat cancers or other diseases. [ABSTRACT FROM AUTHOR]

Background: In recent years, an increasing number of studies have indicated a bidirectional relationship between gut microbiota and the kidneys (the gut-kidney axis). Currently, the potential causal relationship between gut microbiota and diabetic nephropathy remains unclear. This study explores the causal effects of gut microbiota on diabetic nephropathy through Mendelian randomization., Methods: We carried out a comprehensive Mendelian Randomization (MR) analysis, drawing on the Genome-wide Association Study (GWAS) data for 196 varieties of gut microbiota and diabetic nephropathy. The primary analytical approach employed was the inverse-variance weighted, supplemented by the MR-Egger, weighted median, simple mode, and weighted mode. We rigorously assessed heterogeneity with Cochran's Q test and examined pleiotropy via MREgger intercept and MR-PRESSO tests. To ensure the reliability of our findings, we conducted funnel plots and leave-one-out analysis., Results: Our study indicates a causal relationship between the increased risk of diabetic nephropathy and specific gut microbiota, including the Bacteroidia (P=0.01892; OR=1.593; 95%CI, 1.080-2.350), Bacteroidales (P=0.01892; OR=1.593; 95%CI, 1.080-2.350), and LachnospiraceaeUCG008 (P=0.01350; OR=1.452; 95%CI, 1.080-1.953). Conversely, potential protective factors include the Proteobacteria (P=0.00397; OR=0.528; 95%CI, 0.342-0.815), Gammaproteobacteria (P=0.00965; OR=0.474; 95%CI, 0.270-0.834), Lentisphaeria (P=0.04417; OR=0.756; 95%CI, 0.576-0.993), Victivallales (P=0.04417; OR=0.756; 95%CI, 0.576-0.993), and Dialister (P=0.00118; OR=0.513; 95%CI, 0.343-0.768)., Conclusion: This study confirms the causal effects of gut microbiota on diabetic nephropathy. Identifying the risk and protective factors within the gut microbiota for diabetic nephropathy offers fresh insights and novel approaches for preventing and treating this condition., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background: Colorectal cancer (CRC) is a malignancy with high incidence and mortality rates globally, categorized into left-sided and right-sided CRC, each exhibiting significant differences in molecular characteristics, clinical manifestations, and prognosis. Methods: This study employed single-cell transcriptomic data and various bioinformatics approaches, such as two-sample Mendelian randomization, reverse Mendelian randomization, colocalization analysis, directed filtering, pseudotime analysis, and intercellular communication analysis. It analyzed cellular-level disparities between left-sided and right-sided CRC, identifying distinct subpopulations with characteristic variations. For these cells, two-sample Mendelian randomization was utilized to explore gene-to-one-sided CRC causality. Results: LUCAT1 was enriched in high-abundance monocyte subpopulations in right-sided CRC and demonstrated potential risk factor status through Mendelian randomization analysis. The specific single-nucleotide polymorphism (SNP) rs10774624 was associated with an increased risk of CRC. Moreover, metabolic pathway analysis revealed that LUCAT1 + monocytes exhibit lower communication activity in the tumor microenvironment and heightened activity in metabolic functions like glycosaminoglycan degradation. Its biological functions are related to the positive regulation of interleukin-6 production and NF-kappa B signaling, among others. Conclusion: This study confirmed a potential causal relationship between LUCAT1 and right-sided CRC risk through Mendelian randomization analysis. These findings provide novel insights into the pathogenesis of right-sided CRC and may aid in developing early detection and treatment strategies for right-sided CRC., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Shang, Xi, Lai and Cheng.)

Background: Colorectal cancer (CRC) ranks among the leading causes of cancerrelated deaths., Objective: This study aimed to illuminate the relationship between DPP7 (also known as DPP2) and CRC through a combination of bioinformatics and experimental methodologies., Methods: A multi-dimensional bioinformatic analysis on DPP7 was executed, covering its expression, survival implications, clinical associations, functional roles, immune interactions, and drug sensitivities. Experimental validations involved siRNA-mediated DPP7 knockdown and various cellular assays., Results: Data from the Cancer Genome Atlas (TCGA) identified high DPP7 expression in solid CRC tumors, with elevated levels adversely affecting patient prognosis. A shift from the N0 to the N2 stage in CRC was associated with increased DPP7 expression. Functional insights indicated the involvement of DPP7 in cancer progression, particularly in extracellular matrix disassembly. Immunological analyses showed its association with immunosuppressive entities, and in vitro experiments in CRC cell lines underscored its oncogenic attributes., Conclusion: DPP7 could serve as a CRC prognosis marker, functioning as an oncogene and representing a potential immunotherapeutic target., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background: Osteoporosis (OP) is an age-related skeletal disease. Kaempferol can regulate bone mesenchymal stem cells (BMSCs) osteogenesis to improve OP, but its mechanism related to disulfidptosis, a newly discovered cell death mechanism, remains unclear., Objective: The study aimed to investigate the biological function and immune mechanism of disulfidptosis- related ribophorin I (RPN1) in OP and to experimentally confirm that RPN1 is the target for the treatment of OP with kaempferol., Methods: Differential expression analysis was conducted on disulfide-related genes extracted from the GSE56815 and GSE7158 datasets. Four machine learning algorithms identified disease signature genes, with RPN1 identified as a significant risk factor for OP through the nomogram. Validation of RPN1 differential expression in OP patients was performed using the GSE56116 dataset. The impact of RPN1 on immune alterations and biological processes was explored. Predictive ceRNA regulatory networks associated with RPN1 were generated via miRanda, miRDB, and TargetScan databases. Molecular docking estimated the binding model between kaempferol and RPN1. The targeting mechanism of kaempferol on RPN1 was confirmed through pathological HE staining and immunohistochemistry in ovariectomized (OVX) rats., Results: RPN1 was abnormally overexpressed in the OP cohort, associated with TNF signaling, hematopoietic cell lineage, and NF-kappa B pathway. Immune infiltration analysis showed a positive correlation between RPN1 expression and CD8+ T cells and resting NK cells, while a negative correlation with CD4+ naive T cells, macrophage M1, T cell gamma delta, T cell follicular helper cells, activated mast cells, NK cells, and dendritic cells, was found. Four miRNAs and 17 lncRNAs associated with RPN1 were identified. Kaempferol exhibited high binding affinity (-7.2 kcal/mol) and good stability towards the RPN1. The experimental results verified that kaempferol could improve bone microstructure destruction and reverse the abnormally high expression of RPN1 in the femur of ovariectomized rats., Conclusion: RPN1 may be a new diagnostic biomarker in patients with OP, and may serve as a new target for kaempferol to improve OP., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Particulate Matters such as PM 10 , PM 2.5 may contain heavy metal oxides and harmful substances that threaten human health and environmental quality. In this paper, we propose a new combined neural network algorithm which based on Elman, echo state network (ESN) and cascaded BP neural network (CBP) to predict PM 10 and PM 2.5 . In order to further improve the performance of the prediction result, we use the simulated annealing algorithm (SA) to optimize the parameters in the combination method to form the optimal combination model. And particle swarm optimization (PSO) is used to optimize the parameters in ESN. The chemical species in the atmosphere which include SO 2 , NO, NO 2 , O 3 and CO in Baiyin, Gansu Province of China are used to test and verify the proposed combined method. The experimental results show that the prediction performance of the combined model presented in this paper is indeed superior to other three neural network models.

NO donors are routinely used as the exogenous source in in vitro studies. However, the kinetics or the dose of NO release from the existing donors is not readily monitored. This complicates the elucidation of the involvement of NO in a biological response. We report herein a series of NO donors (NOD545a-g), whose NO release is triggered by UV light at 365 nm or a two-photon laser at 740 nm, and importantly, their NO release is accompanied by a drastic fluorescence turn-on, which has been harnessed to follow the kinetics and dose of NO release in a real-time fashion with spectroscopic methods or microscopic methods in in vitro studies. These merits have rendered NOD545a-g useful molecular tools in NO biology.

Classic fluorescent dyes, such as coumarin, naphthalimide, fluorescein, BODIPY, rhodamine, and cyanines, are cornerstones of various spectroscopic and microscopic methods, which hold a prominent position in biological studies. We recently found that 9-amino-benzo[c]cinnoliniums make up a novel group of fluorophores that can be used in biological studies. They are featured with a succinct conjugative push-pull backbone, a broad absorption band, and a large Stokes shift. They are potentially useful as a small-molecule alternative to R-phycoerythrin to pair with fluorescein in multiplexing applications.