Minimum Root Mean Square Research Articles

Investigating the performance of water-mounted solar photo-voltaic systems using different simulation tools

Water-mounted solar photovoltaics plants, which include canal-top and floating systems, offer several advantages over traditional ground mounted systems. However, studying their feasibility can be challenging due to limitations in commercial simulation tools. These tools often rely on unrealistic parameters, and studies comparing them have concluded that they may not be suitable for simulating these newer systems. This research addresses the above-mentioned gap by developing improved simulation methods for water–mounted solar plants. The study compared the actual performance of a 1 MW canal-top system and a 732 kW floating system in India with simulations from six different commercial tools. The analysis revealed that PVsyst and SolarGIS were the most accurate in predicting performance for the canal-top system, while PVsol and SolarGIS–Prospect performed best for the floating system. By following comprehensive methodology outlined in this study, minimal Root–Mean–Square error values ranging from 0.74 to 3.33 for the canal-top system and 2.13 to 5.20 for the floating system were achieved for different parameters. These results highlight that accurate performance predictions depend on three factors: the precision of meteorological data, the capabilities of the simulation tool for water-mounted systems, and the tool’s ability to be customized for specific system details. This study helps solar developers and researchers choose the most suitable simulation tools and customize them for feasibility studies. It also helps stakeholders predict the energy output of existing water–mounted systems more accurately, reducing the risk of penalties due to deviations from predicted output.

Optimization of ultrasound-assisted extraction of bioactive compounds from Carthamus caeruleus L. rhizome: Integrating central composite design, Gaussian process regression, and multi-objective Grey Wolf optimization approaches

The prediction of ultrasound-assisted extraction (UAE) for total phenolic content (TPC) and total flavonoid content (TFC) from Carthamus caeruleus L. rhizomes was conducted using a Gaussian process regression model (GPR) with a multi-objective Grey Wolf optimization approach (MOGWO). A central composite design (CCD) was employed first, examining ethanol concentration, temperature, time, and solvent-to-solid ratio as independent variables. TPC and TFC responses were analyzed under various conditions, revealing significant quadratic and interaction effects (p < 0.05). The GPR was then utilized to predict TPC and TFC, showing high accuracy with correlation coefficients near 1 and minimal root mean square error (RMSE) values. To simultaneously maximize TPC and TFC, the MOGWO was used in a multi-objective framework. Validation through CCD and GPR highlighted GPR's superior predictive accuracy. Optimal conditions (10% ethanol, 40°C, 20minutes sonication, and 50mLg-1 solvent to solid ratio) showed significant discrepancies in CCD predictions but high accuracy in GPR predictions. An interactive tool predicts TPC and TFC using CCD and GPR models. Users input extraction parameters and receive predictions, with a GWO-based optimization module for optimal conditions. The interface enables model comparison, improves process understanding, and optimizes bioactive compound extraction.

Enhancing prediction of elemental composition through machine learning decision tree models for biomass gasification optimization

Abstract The worldwide transition to cleaner, sustainable energy sources, prompted by population growth and industrialization, responds to uncertain fossil fuel prices and environmental concerns, highlighting the substantial benefits of renewable energy in reducing greenhouse gas emissions and addressing climate change. Derived from non-fossilized organic materials, biomass emerges as a significant and sustainable contributor to renewable energy. Its diverse nature is complemented by a range of conversion technologies, encompassing combustion, pyrolysis, gasification, and liquefaction, providing versatile avenues for biomass energy transformation. Gasification, the transformative process of converting organic matter into combustible gases under controlled oxygen levels, is accomplished through direct oxygen supply or pyrolysis. This method yields a dependable gaseous fuel versatile for heating, industrial processes, power generation, and liquid fuel production. Machine learning employs advanced statistical techniques for modeling across diverse industries, showcasing particular efficacy in optimizing thermochemical processes by precisely identifying the optimal operating conditions required for achieving desired product properties. These models utilize proximate biomass data to predict the elemental compositions of N2 and H2. Assessment of both single and two hybrid models indicated that the introduced optimizers significantly enhanced the estimation of N2 and H2 when combined with Decision Tree (DT), with Decision Tree Coupled with Artificial Hummingbird Algorithm (DTAH) proving particularly effective. Notably, DTAH demonstrated outstanding performance with remarkable R 2 values of 0.990 for N2 and 0.992 for H2. Additionally, the minimal Root Mean Square Error (RMSE) values of 1.291 and 1.550 for N2 and H2 predictions respectively underscore the precision of DTAH, establishing it as a suitable choice for practical real-world applications.

Sea clutter prediction based on fusion of Fourier transform and graph neural network

ABSTRACT Radar is a crucial tool for remote sensing and monitoring of marine environments. However, its effectiveness is significantly influenced by sea clutter. The complex interplay between radar parameters and various maritime environmental factors gives rise to a dynamic and intricate sea clutter pattern. The conventional approach to sea clutter prediction only considers the temporal dependence, neglecting the spatial changes. To address this limitation, this study proposes the Fusion of Fourier Transform and Graph Neural Network (FFTaGNN) to enhance the accuracy of multi-dimensional sea clutter data forecasting. FFTaGNN captures the correlations and time dependencies among sequences in the spectral domain. By combining the discrete Fourier transform (DFT) and graph Fourier transform (GFT), it extracts the temporal correlation characteristics and establishes correlations between multidimensional sea clutter data sequences. Importantly, FFTaGNN can automatically discover data correlations between sequences without relying on predetermined priors. To validate the effectiveness of the model, an experimental verification process is conducted, considering different grazing angles and sea clutter High Range Resolution Profile (HRRP) data. The results of the experiment demonstrate that the proposed strategy achieves a minimum Root Mean Square Error (RMSE) of 0.0574 in predicting sea clutter HRRP data. This technique holds great potential in effectively suppressing sea clutter, thereby enhancing the overall performance of radar systems in marine environments and small target detection capabilities at the sea surface.

Structural and Functional Annotations of Hypothetical Proteins of Candida auris for Novel Drug Target Identification: An insilico Approach

Candida auris is an emerging fungal pathogen that causes severe invasive infections in healthcare facilities which are difficult to control and treat due to its resistance to major antifungal drugs. A large fraction of C. auris proteins are uncharacterized and hypothetical, and structural and functional characterization of these proteins can aid in the selection of novel drug targets. The present study involves a computational approach for the structural and functional characterization of hypothetical proteins from Candida auris. After the sequenceretrieval, hypothetical proteins were predicted for physiochemical properties and subcellular localization, structurally modeled using I-TASSER, quality assessed through Verify3D, ERRAT, and PROCHECK, and functionally annotated to reveal conserved domains and roles in pathogen pathways. Finally, the immunogenic assessments, non-human homologous analysis, and druggability analysis reveal three hypothetical proteins of C. auris (KND95408.2, KND95415.2, and KND95429.2) as novel drug targets. Furthermore, the stable conformations of theseselected drug targets with the minimum root mean square deviations (RMSD) and fluctuation (RMSF) were analyzed by MD simulations. Conclusively, the functional annotations of these hypothetical proteins can help in understanding the disease mechanisms at the molecular level, as well as provide new targets for drug development against Candida auris.

Optimizing production efficiency in semiconductor enterprises by an improve and optimized biogeographical optimization algorithm based on three-layer coding

Aiming at the problem of low production efficiency and inability to fully unleash production capacity in current semiconductor enterprises, a production efficiency optimization model for semiconductor enterprises has been studied and constructed. This model transforms the problem of low production efficiency into a problem of locating and solving the decoupling point of enterprise customer orders, and comprehensively considers the situation of sudden changes in enterprise production orders when locating and solving the decoupling point of customer orders. Propose to use a three-layer coding (TLC) mechanism to improve and optimize the biogeographical optimization algorithm, and use the improved biogeographical optimization (IOBO) algorithm to solve the production efficiency optimization problem of semiconductor enterprises. The results show that the proposed IBBO-TCL algorithm has a fast convergence speed and the minimum root mean square error after convergence. And this method can accurately solve the decoupling point of customer orders for semiconductor enterprises. The method proposed in the study has effectively improved the production efficiency of semiconductor enterprises and has guiding significance for optimizing enterprise structure.

A hybrid machine learning-CFD method for the innovative analysis of Al2O3 nanoparticle deposition in shell-and-tubes heat exchangers

This study examines the intricate dynamics surrounding the deposition of Al2O3 nanoparticles within a heat exchanger, with the aim of optimizing heat transfer efficiency and gaining insights into gas dynamics. A comprehensive investigation of various parameters is conducted, including nanoparticle diameter ranging from 10 to 100 nm, heat flux variations from 500 to 3000 W/m2, Reynolds numbers spanning from 308 to 1540, and mass fractions ranging from 0.5 to 8 %. The methodology integrates machine learning algorithms with Eulerian and Lagrange methods, leveraging Python programming to deepen the understanding of complex deposition processes. Through the integration of random forest algorithms and SHAP values, the study achieves a model accuracy of 96.74 %, supported by minimal mean absolute error (6E-06) and root mean square error (2.5E-03). Key findings reveal the profound impact of heat flux, particularly at 3000 W/m2, on enhancing nanoparticle deposition. Furthermore, a direct correlation is observed between mass fraction and sedimentation, peaking at a mass fraction of 8 %. In laminar flow regimes, the Reynolds number profoundly influences sedimentation, with the sedimentation rate reaching its apex as the Reynolds number decreases. The diameter of nanoparticles also emerges as a crucial factor, with larger diameters correlating with increased sedimentation.

Experimental and thermodynamic study of amyl acetate, amyl acetoacetate, and isoamyl acetoacetate in CO2 solvent

Acetate ester compounds find widespread use in various applications such as surface coatings, ink formulations, pharmaceuticals, and adhesives. It is essential to investigate the phase transition behavior of amyl acetate, amyl acetoacetate, and isoamyl acetoacetate in high-pressure supercritical CO2 (SC-CO2). The vapor-liquid equilibria (VLE) of the SC-CO2 + amyl acetate, SC-CO2 + amyl acetoacetate, and SC-CO2 + isoamyl acetoacetate systems were examined at different temperatures (313.2 ≤ T ≤ 393.2 K) and pressures (1.67 ≤ P ≤ 20.76 MPa). The solubility curve of these systems shows Type-I phase behavior, and the critical points of these binary mixtures were observed between the critical properties of the pure components involved in the systems. The solubility of amyl acetate, amyl acetoacetate, and isoamyl acetoacetate in the SC-CO2 + amyl acetate, SC-CO2 + amyl acetoacetate, and SC-CO2 + isoamyl acetoacetate systems increases with increasing temperature at constant pressure. The two-parameter model of Peng-Robinson equation of state along with a mixing rule, accurately correlated the phase transition behavior and critical mixtures curves for all three systems. The binary interaction parameters (BIPs) were adjusted, and the minimum root mean square deviation percentage was identified for all three systems. The calculated error% was found to be within reasonable limits, with values of 4.95 %, 3.93 %, and 4.18 % for SC-CO2 + amyl acetate, SC-CO2 + amyl acetoacetate, and SC-CO2 + isoamyl acetoacetate systems, respectively. Furthermore, the interaction parameters for the SC-CO2 + amyl acetoacetate mixture was found to be temperature-dependent, and the tested linear regression correlation coefficient for the BIPs parameters of (kij) and (ηij) are 0.98533 and 0.99083, respectively. This is the first research study on the phase behavior of acetate ester compounds in SC-CO2 solvents, and the results have a significant impact on industries at different operating conditions.

An accelerated sequential function specification method based on LM gradient for transient inverse heat conduction problem

The sequential function specification method (SFSM) and the Levenberg-Marquardt method (LM) are two typical methods for inverse heat conduction problems (IHCP). However, these two methods have poor convergence and low convergence speed. Therefore, the combination of LM with SFSM is depended on to propose a modified Sequential Levenberg-Marquardt gradient method (SLM). The computation results of three shapes of heat fluxes with noise show the SLM is averagely improved over 16.3% more than SFSM in computation accuracy, but equally reduced over 39.9% less than in computation time. On other hand, when the emphasis is on the effect of the Aitken acceleration method, Tikhonov regularization and polynomial order on SLM, a neural network prediction model of the root mean square error is trained by regularization factor, polynomial order and the number of future time steps. Subsequently, the genetic algorithm optimization method is proposed for the minimum root mean square error so that it can improve the computation accuracy and efficiency of IHCP to the maximum extent. Finally, Second-order polynomial order and small order-of-magnitude regularization factor are highly recommended for parameter selection with multiple choices. The ASLM only needs to determine the optimal number of future time steps.

In-silico study unveils potential phytocompounds in Andrographis paniculata against E6 protein of the high-risk HPV-16 subtype for cervical cancer therapy

Despite therapeutic advancements, cervical cancer caused by high-risk subtypes of the human papillomavirus (HPV) remains a leading cause of cancer-related deaths among women worldwide. This study aimed to discover potential drug candidates from the Asian medicinal plant Andrographis paniculata, demonstrating efficacy against the E6 protein of high-risk HPV-16 subtype through an in-silico computational approach. The 3D structures of 32 compounds (selected from 42) derived from A. paniculata, exhibiting higher binding affinity, were obtained from the PubChem database. These structures underwent subsequent analysis and screening based on criteria including binding energy, molecular docking, drug likeness and toxicity prediction using computational techniques. Considering the spectrometry, pharmacokinetic properties, docking results, drug likeliness, and toxicological effects, five compounds—stigmasterol, 1H-Indole-3-carboxylic acid, 5-methoxy-, methyl ester (AP7), andrographolide, apigenin and wogonin—were selected as the potential inhibitors against the E6 protein of HPV-16. We also performed 200 ns molecular dynamics simulations of the compounds to analyze their stability and interactions as protein–ligand complexes using imiquimod (CID-57469) as a control. Screened compounds showed favorable characteristics, including stable root mean square deviation values, minimal root mean square fluctuations and consistent radius of gyration values. Intermolecular interactions, such as hydrogen bonds and hydrophobic contacts, were sustained throughout the simulations. The compounds displayed potential affinity, as indicated by negative binding free energy values. Overall, findings of this study suggest that the selected compounds have the potential to act as inhibitors against the E6 protein of HPV-16, offering promising prospects for the treatment and management of CC.

Failure evaluation on tailor made aerospace aluminum alloys via underwater friction stir welding employing predictive machine learning technologies

Employing tailor-made alloys with uneven thickness achieves light weighting, a critical issue for reducing emissions, leading to lower aircraft pollutants and fuel costs. The research utilizes advanced machine learning techniques such as Gaussian process regression (GPR), artificial neural networks (ANN) linear regression (LR), and support vector machines (SVM) to predict the ultimate tensile strength of underwater friction stir welding of AA6082-T6 and A2219-T83 tailor-made joints. The models have been evaluated with an assortment of kernel functions, including the polynomial kernel (PK), the radial basis function (RBF), and the Pearson VII universal kernel (PUK). To acquire experimental data, we used a Central Composite Design (CCD) technique, incorporating various factors in the process encompassing tool tilt angle (TA), rotating speed (RS), and welding speed (WS). The SVM radial basis function model (SRBP) had a maximum correlation coefficient of 0.9995 and a minimum root mean square error value (RMSE) of 0.5433 in the training set and 0.6271 in the test set. The ANN model predicted the UTS with an error margin of 0.21%, while the SRBP model showed a 0.52% error, and the LR model exhibited a significantly higher error of 7.73%. A peak tensile strength of 252.98 MPa was recorded in the S20 specimen, accounting for 85.61% of the base metal’s (AA6082 T6) strength. A reduced acute tearing ridge indicates petite, shallow dimples due to the inherent cooling. Through the analysis of metrics and residuals, high accuracy rates were observed when employing the ANN and SRBP models to predict mechanical traits.

Spatial Downscaling of Nighttime Land Surface Temperature Based on Geographically Neural Network Weighted Regression Kriging

Land surface temperature (LST) has a wide application in Earth Science-related fields, and spatial downscaling is an important method to retrieve high-resolution LST data. However, existing LST downscaling methods have difficulties in simultaneously constructing and expressing spatial non-stationarity, spatial autocorrelation, and complex non-linearity during the LST downscaling process, which limits the performance of the models. Moreover, there is a lack of research on high-resolution nighttime land surface temperature (NLST) reconstruction based on spatial downscaling, which does not meet the data needs for urban-scale nighttime urban heat island (UHI) studies. Therefore, this study combined Geographically Neural Network Weighted Regression (GNNWR) with Area-to-Point Kriging interpolation (ATPK) to propose a Geographically Neural Network Weighted Regression Kriging (GNNWRK) model for NLST downscaling. To verify the model’s generality and robustness, this study selected four study areas with different landform and climate type for NLST spatial downscaling experiments. The GNNWRK was compared with four benchmark downscaling methods, including TsHARP, Random Forest, Geographically Weighted Regression, and GNNWR. The results show that compared to these four benchmark methods, the GNNWRK method has higher accuracy in NLST downscaling, with a maximum Pearson’s Correlation Coefficient (Pcc) of 0.930 and a minimum Root Mean Square Error (RMSE) of 0.886 K. Moreover, the validation based on MODIS NLST data and ground-measured NLST data also indicates that the GNNWRK model can obtain more accurate, high-resolution NLST with richer and more detailed texture. This enhances the potential of NLST in studying the effects of urban nighttime heat islands at a finer scale.

A SAR and QSAR study on 3CLpro inhibitors of SARS-CoV-2 using machine learning methods

ABSTRACT The 3C-like Proteinase (3CLpro) of novel coronaviruses is intricately linked to viral replication, making it a crucial target for antiviral agents. In this study, we employed two fingerprint descriptors (ECFP_4 and MACCS) to comprehensively characterize 889 compounds in our dataset. We constructed 24 classification models using machine learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), extreme Gradient Boosting (XGBoost), and Deep Neural Networks (DNN). Among these models, the DNN- and ECFP_4-based Model 1D_2 achieved the most promising results, with a remarkable Matthews correlation coefficient (MCC) value of 0.796 in the 5-fold cross-validation and 0.722 on the test set. The application domains of the models were analysed using dSTD-PRO calculations. The collected 889 compounds were clustered by K-means algorithm, and the relationships between structural fragments and inhibitory activities of the highly active compounds were analysed for the 10 obtained subsets. In addition, based on 464 3CLpro inhibitors, 27 QSAR models were constructed using three machine learning algorithms with a minimum root mean square error (RMSE) of 0.509 on the test set. The applicability domains of the models and the structure-activity relationships responded from the descriptors were also analysed.

Theoretical framework and inference for fitting extreme data through the modified Weibull distribution in a first-failure censored progressive approach

The importance of biomedical physical data is underscored by its crucial role in advancing our comprehension of human health, unraveling the mechanisms underlying diseases, and facilitating the development of innovative medical treatments and interventions. This data serves as a fundamental resource, empowering researchers, healthcare professionals, and scientists to make informed decisions, pioneer research, and ultimately enhance global healthcare quality and individual well-being. It forms a cornerstone in the ongoing pursuit of medical progress and improved healthcare outcomes. This article aims to tackle challenges in estimating unknown parameters and reliability measures related to the modified Weibull distribution when applied to censored progressive biomedical data from the initial failure occurrence. In this context, the article proposes both classical and Bayesian techniques to derive estimates for unknown parameters, survival, and failure rate functions. Bayesian estimates are computed considering both asymmetric and symmetric loss functions. The Markov chain Monte Carlo method is employed to obtain these Bayesian estimates and their corresponding highest posterior density credible intervals. Due to the inherent complexity of these estimators, which cannot be theoretically compared, a simulation study is conducted to evaluate the performance of various estimation procedures. Additionally, a range of optimization criteria is utilized to identify the most effective progressive control strategies. Lastly, the article presents a medical application to illustrate the effectiveness of the proposed estimators. Numerical findings indicate that Bayesian estimates outperform other estimation methods by achieving minimal root mean square errors and narrower interval lengths.

Immuno-informatics study identifies conserved T cell epitopes in non-structural proteins of Bluetongue virus serotypes: formulation of a computationally optimized next-generation broad-spectrum multi-epitope vaccine.

Bluetongue (BT) poses a significant threat to the livestock industry, affecting various animal species and resulting in substantial economic losses. The existence of numerous BT virus (BTV) serotypes has hindered control efforts, highlighting the need for broad-spectrum vaccines. In this study, we evaluated the conserved amino acid sequences within key non-structural (NS) proteins of BTV and identified numerous highly conserved murine- and bovine-specific MHC class I-restricted (MHC-I) CD8+ and MHC-II-restricted CD4+ epitopes. We then screened these conserved epitopes for antigenicity, allergenicity, toxicity, and solubility. Using these epitopes, we developed in silico-based broad-spectrum multiepitope vaccines with Toll-like receptor (TLR-4) agonists. The predicted proinflammatory cytokine response was assessed in silico using the C-IMMSIM server. Structural modeling and refinement were achieved using Robetta and GalaxyWEB servers. Finally, we assessed the stability of the docking complexes through extensive 100-nanosecond molecular dynamics simulations before considering the vaccines for codon optimization and in silico cloning. We found many epitopes that meet these criteria within NS1 and NS2 proteins and developed in silico broad-spectrum vaccines. The immune simulation studies revealed that these vaccines induce high levels of IFN-γ and IL-2 in the vaccinated groups. Protein-protein docking analysis demonstrated promising epitopes with strong binding affinities to TLR-4. The docked complexes were stable, with minimal Root Mean Square Deviation and Root Mean Square Fluctuation values. Finally, the in silico-cloned plasmids have high % of GC content with > 0.8 codon adaptation index, suggesting they are suitable for expressing the protein vaccines in prokaryotic system. These next-generation vaccine designs are promising and warrant further investigation in wet lab experiments to assess their immunogenicity, safety, and efficacy for practical application in livestock. Our findings offer a robust framework for developing a comprehensive, broad-spectrum vaccine, potentially revolutionizing BT control and prevention strategies in the livestock industry.

Hybrid Tiki Taka and Mean Differential Evolution based Weibull distribution: A comprehensive approach for solar PV modules parameter extraction with Newton-Raphson optimization

Effective parameter extraction is crucial in modeling and analyzing complex systems, particularly photovoltaic (PV) cells/modules. Therefore, it is essential to develop a robust optimization model that can effectively determine the optimal parameters of PV models. This study introduces a novel metaheuristic algorithm, Tiki Taka Algorithm (TTA), inspired by sports strategies, particularly the renowned tiki-taka style. The innovation lies in the hybridization of TTA with the Mean Differential Evolution based on Weibull distribution (MDEW). The resultant optimal parameters obtained from this hybridization serve as the initial guess for Newton-Raphson (NR), a critical step that ensures the estimated current closely aligns with the measured current, leading to a minimized Root Mean Square Error (RMSE). The hybrid approach leverages the excellent exploration capabilities of Weibull distribution and the exploitation strength of the Mean Differential Evolution (MeanDE) mutation, offering a comprehensive solution for navigating complex optimization landscapes. The proposed TTA-MDEW effectively identifies various parameters in PV models, including single diodes, double diodes, and PV modules. The findings show that TTA-MDEW holds its ground against the most sophisticated optimization methods in terms of reliability, accuracy, and speed of convergence. Additionally, data from real-world manufacturer datasheets reveal that our method delivers exceptionally precise results under varying irradiance and temperature conditions. Therefore, these outcomes establish the TTA-MDEW as an effective tool for extracting precise parameters from various PV models, enhancing the modeling of PV systems.

Changes in monthly surface area, water level, and storage of 194 lakes and reservoirs in the Yangtze River Basin during 1990–2021 using multisource remote sensing data

Long-term, high spatiotemporal resolution of surface water area, water level, and storage changes in the Yangtze River Basin (YRB) has great scientific and practical importance for improving the management of water resources. Here, three distinct area estimations were first derived using the water classification enhancement method, automated water extraction method based on random forest, and the modified normalized difference water index. The optimized area data was determined by comparing against Sentinel-2 with the minimum root mean square error. A new area data was constructed with the optimized area as the primary data, while the remaining datasets were employed to fill in gaps. The elevation-area relationship was used to derive monthly water level. Changes in water storage were calculated by applying the pyramidal frustum formula from surface water area and water level data. Finally, a new comprehensive dataset of the monthly area, level, and storage changes in the 119 lakes and 75 reservoirs across the YRB with area larger than 10 km2 from 1990 to 2021 were first reconstructed. The spatiotemporal trends of surface water area/level/storage in lakes and reservoirs over 11 sub-basins of the YRB were quantified from 1990 to 2021, as well as before (1990–2003) and after (2003−2021) the construction of the Three Gorges Dam (TGD). During 1990–2021, there was a marked decrease in surface water area/level/storage in most of the YRB sub-basins, which contain 79 % of the lakes and 30 % of the reservoirs. After TGD was constructed, the surface water in lakes decreased by 10 %, while that of reservoirs remained consistent with the pre-construction. The surface water area/level/storage in the lower sub-basins of YRB exhibited a decline to an upward trend before and after the construction of TGD. This study provides a new comprehensive dataset for understanding the dynamic changes of water resource and climate change.

Estimation Approach for a Linear Quantile-Regression Model with Long-Memory Stationary GARMA Errors

The aim of this paper is to assess the significant impact of using quantile analysis in multiple fields of scientific research . Here, we focus on estimating conditional quantile functions when the errors follow a GARMA (Generalized Auto-Regressive Moving Average) model. Our key theoretical contribution involves identifying the Quantile-Regression (QR) coefficients within the context of GARMA errors. We propose a modified maximum-likelihood estimation method using an EM algorithm to estimate the target coefficients and derive their statistical properties. The proposed procedure yields estimators that are strongly consistent and asymptotically normal under mild conditions. In order to evaluate the performance of the proposed estimators, a simulation study is conducted employing the minimum bias and Root Mean Square Error (RMSE) criterion. Furthermore, an empirical application is given to demonstrate the effectiveness of the proposed methodology in practice.

Investigating emodin derivatives against SARS-CoV-2 found in medicinal herbs

SARS-CoV-2 continues to mutate and circulate at alarming levels around the world and is exemplified by the emergence of the new variant JN.1. The emergence of novel SARS-CoV variants underscores the urgency for the development of new drugs. Phytochemicals, known for their safety and efficacy, have been widely used to address various ailments. This study aims at identifying the anti-SARS-CoV potential of emodin, a bioactive anthraquinone, and its derivatives. Key proteins, including hemagglutinin-esterase (HE), papain-like protease (PLpro), major protease (3CLpro), non-structural protein (nsp3) and spike protein(S) of SARS-CoV, were used as protein targets for the virtual screening of 110 emodin derivatives (ED) and remdesivir a proven antiviral drug. Among the 110 derivatives, ED21, ED25, ED5 inhibited HE, 3CLpro and S protein, respectively. ED29 inhibited both PLpro and nsp3 with high binding affinity. Similar receptor binding sites were occupied by remdesivir and emodin derivatives. These emodin derivatives are bound to similar amino acids in receptors as compared to remdesivir. Further dynamic simulations studies with ED21-HE, ED25-3CLpro, ED5-S, ED29-PLpro and ED29-nsp3 complexes showed all of them are stable with minimum root mean square deviation (RMSD), root mean square fluctuation (RMSF), solvent-accessible surface area (SASA) and radius of gyration (Rg) and are comparable to APO protein. All the ligands accepted Lipinski's rule of five. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis revealed only ED21 and ED5 as promising druglike candidates that can have high therapeutic value and no side effects. These in-silico findings contribute to the development of potent SARS-CoV-2 inhibitors, potentially advancing the quest for effective antiviral drugs.

Design of Honeycomb‐Filled End Plate for Proton Exchange Membrane Fuel Cells Based on Topology Optimization

Designing a functional end plate for the proton exchange membrane fuel cell (PEMFC) is a challenging task since the structure that simultaneously satisfies uniform contact pressure and lightweight features must be achieved. To design an end plate for both requirements, a multi‐objective topology optimization model of the PEMFC based on the concept of equivalent stiffness is established with the aim of maximum stiffness and minimum root mean square (RMS) of contact displacement. Based on the final topology, the filled honeycomb‐based end plates with excellent mechanical properties and energy absorption effects are optimized through the particle swarm optimization algorithm (PSO). The results demonstrate that the end plate mass is reduced by 39.5% and the stress distribution is more uniform. The standard deviation of pressure decreases by 30.8% compared to the original model data. The proposed optimization method is helpful in the design of future end plates.