Accurate Results Research Articles

Applying Proper Orthogonal Decomposition to Parabolic Equations: A Reduced Order Numerical Approach

n this paper we present a low-order numerical scheme developed using the Proper Orthogonal (POD) method to address non-homogeneous parabolic equations in both one and two dimensions. The proposed schemes leverage the POD technique to reduce the computational complexity associated with solving these equations while maintaining accuracy. By employing POD, the high-dimensional problem is approximated by a reduced set of models, allowing for a more efficient representation of the system dynamics. The application of this method to non-homogenous parabolic equations offers a promising approach for enhancing the computational efficiency of simulations in diverse fields, such as fluid dynamics, heat conduction, and reaction-diffusion processes. The presented numerical scheme demonstrates its efficacy in achieving accurate results with significantly reduced computational costs, making it a valuable tool for applications demanding efficient solutions to non-homogeneous parabolic equations in one and two dimensions.

Remaining Useful Life Prediction of Rolling Bearing Based on Multi-Domain Mixed Features and Temporal Convolutional Networks

For the remaining useful life (RUL) prediction of rolling bearing under strong background noise, it is hard to get accurate results based on the non-stationary vibration signals because of complex degradation characteristics and difficult extraction of key features. The framework of RUL prediction for rolling bearing is established by integrating multi-domain mixed features and temporal convolutional network (TCN). The variational mode decomposition method based on the dung beetle optimization algorithm is developed to reduce signal noise by determining the optimal parameters adaptively. To construct a health indicator of rolling bearing effectively, an isometric feature mapping algorithm is introduced to reduce the dimensionality of multi-domain mixed features, integrating time-domain, frequency-domain, and entropy features of vibration signals under non-stationary and nonlinear conditions. By considering the advantages of a multi-head attention mechanism (MA) and bidirectional gated recurrent unit (BiGRU), a TCN-based multi-head attention and bidirectional gate (TCNMABG) is developed to predict the RUL of rolling bearing accurately, whose detailed implementation process of TCNMABG is described based on XJTU-SY dataset. To verify the performance of TCNMABG, the FEMTO-ST dataset is introduced to perform the numerical experiments, and the results show that prediction error is reduced by 65.96% on average.

Research on Delamination Damage Quantification Detection of CFRP Bending Plate Based on Lamb Wave Mode Control.

The carbon-fiber-reinforced polymer (CFRP) bending structure is widely used in aviation. The emergence and spread of delamination damage will decrease the safety of in-service bending structures. Lamb waves can effectively identify delamination damage as a high-damage-sensitivity detection tool. For this present study, the signal difference coefficient (SDC) was introduced to quantify delamination damage and evaluate the sensitivity of A0-mode and S0-mode Lamb waves to delamination damage. The simulation results show that compared with the S0-mode Lamb wave, the A0-mode Lamb wave exhibits higher delamination damage sensitivity. The delamination damage can be quantified based on the strong correlation between the SDC and the delamination damage size. The control effect of the linear array PZT phase time-delay method on the Lamb wave mode was investigated by simulation. The phase time-delay method realizes the generation of a single-mode Lamb wave, which can separately excite the A0-mode and S0-mode Lamb wave to identify delamination damage of different sizes. The A0-mode Lamb wave was excited by the developed one-dimensional miniaturized linear comb transducer (LCT), which was used to conduct the detection experiment on the CFRP bending plate with delamination damage sizes of Φ6.0 mm, Φ10.0 mm, and Φ15.0 mm. The experimental results verify the correctness of the simulation. According to the Hermite interpolation results of the finite-element simulation data, the relationship between the delamination damage size and the SDC was fitted by the Gaussian function and Rational function, which can accurately quantify the delamination damage. The absolute error of the delamination damage quantification with Gaussian and Rational fitting expression does not exceed 0.8 mm and 0.7 mm, and the percentage error is not more than 8% and 7%. The detection and signal processing methods employed in the present research are easy to operate and implement, and accurate delamination damage quantification results have been obtained.

PROPHETIC MEDICINE AND HERBS IN ORAL CARE A REVIEW OF LITERATURE

Prophetic medicine comprises the teaching of the prophet Mohammad with regards to nutrition, hygiene, sickness, cure and prevention of the ailments. “Whenever the Prophet (ﷺ) would visit an ill person, he would said:‘[O Allah,] make the harm go away, Lord of mankind, and heal him, You are the Healer, there is no healing except your healing, a healing that does not leave any sickness. Healing with the Medicine of the Prophet (PBUH) is the remedy for well-being, good health and soul. Prophetic medicines promote healing, prevention and cure of ailments. The review article is an effort to explore detailed analysis of prophetic medicines and natural herbs in oral care. The article is focused on the uses of miswak, honey, black seed, olives, senna and idhkhir for oral therapeutic uses. The studies, experiments and research have demonstrated that the prophetic medicines and herbs provide benefits in oral care. However, the research conducted on some herbs are limited to in vitro studies, thus further studies are required to be carried out on animals or humans to achieve accurate results. Furthermore, other herbs mentioned in prophetic medicines like tannumah (aloes), daghabis (dill seed), dates, cortus, need to be explored and further studied for therapeutic use in oral care.

Materiality assessment in sport – an opportunity and call for research

ABSTRACT Research question Materiality assessment is a tool widely used among practitioners to determine an organization's most important sustainability topics. The tool helps visualize trade-offs and develop a prioritization of issues based on stakeholder engagement and impact measurement. Ultimately, it aids in the creation of a sustainability strategy. Although widely used in practice, the tool has been absent from the sport management literature. Research methods In this rapid communication, we outline the steps of materiality assessment, the tool’s limitations, and research opportunities to overcome these issues. Results and Findings The tool’s limitations include a lack of theoretical foundations, imprecise methodological processes, and a lack of transparency in the process and output communication. Implications These limitations can perpetuate injustice, inaccurate results, mistrust, and allegations of greenwashing. However, while these issues present obstacles for practitioners, they also create research opportunities. Specifically, scholars can assist by creating a list of sport sector-specific material topics to be evaluated, identifying stakeholders and robust ways of engagement, developing a rigorous process for impact assessment, and aiding in the transparent reporting of results. Ultimately, by overcoming the limitations of materiality assessment, research can help advance practice and positively contribute to sustainable development.

Selection of best wavelet functions and decomposition levels for coupling with soft computing methods in estimating ETo in coastal and island regions

ABSTRACT Mass transfer-based models are among the extensively applied reference evapotranspiration (ET o ) estimation approaches that have been proven to provide reliable estimates under certain climatic and geographical conditions. A major issue with such approaches is the high variations in wind speed time series, which is a governing meteorological parameter in mass transfer-based models. few studies have focused on applying wavelet transform as a robust pre-processing technique for ETo estimation. A major question with coupling wavelet transforms with soft computing methods would be the proper choice of the wavelet function, vanishing moments and the decomposition levels. The present study aims at assessing different wavelet functions coupled with boosted regression tree (BT) models, i.e. wavelet-BT (WBT), in estimating ET o values (through mass transfer parameters) in both coastal and island regions, where it is supposed that wind speed variations are affected by sea–inland interactions and so has the sharp variations (unlike ET o ). The obtained results showed that the coupled WBT model could improve the single BT model results to a great extent, and meanwhile, the Daubechies wavelet function with six vanishing moments provided the most accurate results in all the locations. Further, the best simulation outcomes with each wavelet function were obtained when the maximum possible decomposition level was used with each function.

Optimal Configuration of Multi-Energy Storage in an Electric–Thermal–Hydrogen Integrated Energy System Considering Extreme Disaster Scenarios

Extreme disasters have become increasingly common in recent years and pose significant dangers to the integrated energy system’s secure and dependable energy supply. As a vital part of an integrated energy system, the energy storage system can help with emergency rescue and recovery during major disasters. In addition, it can improve energy utilization rates and regulate fluctuations in renewable energy under normal conditions. In this study, the sizing scheme of multi-energy storage equipment in the electric–thermal–hydrogen integrated energy system is optimized; economic optimization in the regular operating scenario and resilience enhancement in extreme disaster scenarios are also considered. A refined model of multi-energy storage is constructed, and a two-layer capacity configuration optimization model is proposed. This model is further enhanced by the integration of a Markov two-state fault transmission model, which simulates equipment defects and improves system resilience. The optimization process is solved using the tabu chaotic quantum particle swarm optimization (TCQPSO) algorithm to provide reliable and accurate optimization results. The results indicate that addressing severe disaster situations in a capacity configuration fully leverages the reserve energy function of energy storage and enhances system resilience while maintaining economic efficiency; furthermore, adjusting the load loss penalty coefficients offers a more targeted approach to the balancing of the system economy and resilience. Thus, new algorithmic choices and planning strategies for future research on enhancing the resilience of integrated energy systems under extreme disaster scenarios are provided.

Production of positronium chloride: A study of the charge exchange reaction between Ps and Cl.

We present cross sections for the formation of positronium chloride (PsCl) in its ground state from the charge exchange between positronium (Ps) and chloride (Cl-) in the range of 10 meV-100eV Ps energy. We have used theoretical models based on the first Born approximation in its three-body formulation. We simulated the collisions between Ps and Cl- using abinitio binding energies and positronic wave functions at both the mean-field and correlated levels extrapolated to the complete basis set limit. The accuracy of these abinitio data was benchmarked on the PsF system with the existing highly accurate results, including the very recent quantum Monte Carlo results. We have investigated Ps excited states up to n = 4. The results suggest that the channel Ps(n = 2) is of particular interest for the production of PsCl in the ground state and shows that an accurate treatment of correlation effects (i.e., electron-electron and electron-positron correlations) leads to a significant change in the magnitude of the PsCl production cross section with respect to the mean-field level.

Development of a multi-wear-site, deep learning-based physical activity intensity classification algorithm using raw acceleration data.

Accelerometers are widely adopted in research and consumer devices as a tool to measure physical activity. However, existing algorithms used to estimate activity intensity are wear-site-specific. Non-compliance to wear instructions may lead to misspecifications. In this study, we developed deep neural network models to classify device placement and activity intensity based on raw acceleration data. Performances of these models were evaluated by making comparisons to the ground truth and results derived from existing count-based algorithms. 54 participants (26 adults 26.9±8.7 years; 28 children, 12.1±2.3 years) completed a series of activity tasks in a laboratory with accelerometers attached to each of their hip, wrist, and chest. Their metabolic rates at rest and during activity periods were measured using the portable COSMED K5; data were then converted to metabolic equivalents, and used as the ground truth for activity intensity. Deep neutral networks using the Long Short-Term Memory approach were trained and evaluated based on raw acceleration data collected from accelerometers. Models to classify wear-site and activity intensity, respectively, were evaluated. The trained models correctly classified wear-sites and activity intensities over 90% of the time, which outperformed count-based algorithms (wear-site correctly specified: 83% to 85%; wear-site misspecified: 64% to 75%). When additional parameters of age, height and weight of participants were specified, the accuracy of some prediction models surpassed 95%. Results of the study suggest that accelerometer placement could be determined prospectively, and non-wear-site-specific algorithms had satisfactory accuracies. The performances, in terms of intensity classification, of these models also exceeded typical count-based algorithms. Without being restricted to one specific wear-site, research protocols for accelerometers wear could allow more autonomy to participants, which may in turn improve their acceptance and compliance to wear protocols, and in turn more accurate results.

Smartphone-assisted hydrogel platform based on BSA-CeO2 nanoclusters for dual-mode determination of acetylcholinesterase and organophosphorus pesticides.

Adual-mode sensor was developed for detecting acetylcholinesterase (AChE) and organophosphorus pesticides (OPs) via bifunctional BSA-CeO2 nanoclusters (NCs) with oxidase-mimetic activity and fluorescence property. The dual-mode sensor has the characteristics of self-calibration and self-verification, meeting the needs of different detection conditions and provide more accurate results. The colorimetric sensor and fluorescence sensor have been successfully used for detecting AChE with limit of detection (LOD) of 0.081mU/mL and 0.056mU/mL, respectively, while the LOD forOPs were 0.9ng/mL and 0.78ng/mL, respectively. The recovery of AChE was 93.9-107.2% and ofOPs was 95.8-105.0% in actual samples. A novel strategy was developed to monitor pesticide residues and detect AChE level, which will motivate future work to explore the potential applications of multifunctional nanozymes.

Why Do Empirical Valence Bond Simulations Yield Accurate Arrhenius Plots?

Computer simulations of the temperature dependence of enzyme reactions using the empirical valence bond (EVB) method have proven to give very accurate results in terms of the thermodynamic activation parameters. Here, we analyze the reasons for why such simulations are able to correctly capture activation enthalpies and entropies and how sensitive these quantities are to parametrization of the reactive potential energy function. We examine first the solution reference reaction for the enzyme ketosteroid isomerase, which corresponds to the acetate catalyzed deprotonation of the steroid in water. The experimentally determined activation parameters for this reaction turn out to be remarkably well reproduced by the calculations. By modifying the EVB potential so that the activation and reaction free energies become significantly shifted, we show that the activation entropy is basically invariant to such changes and that ΔS⧧ is instead determined by the specific mixture of the underlying force fields in the transition state region. The coefficients of this mixture do not change appreciably when the EVB potential is modified within reasonable limits, and hence, the estimate of ΔS⧧ becomes very robust. This is further verified by examining a more complex concerted hydride and proton transfer reaction in the enzyme hydroxybutyrate dehydrogenase.

Light one-electron molecular ions within the finite-basis-set method for the two-center Dirac equation

During the last decades outstanding results on the precision description of light diatomic molecular compounds have been achieved. The most advanced calculations of electron binding energies have been realized mainly in the framework of the nonrelativistic approach with a consistent account of relativistic and radiative QED corrections. Recently, it has been shown that methods based on the Dirac equation are also suitable for obtaining highly accurate results in simple light molecules. In this paper, we present a completely relativistic method and discuss its application to the description of diatomic systems. In particular, the electronic spectra of the light one-electron quasi-molecular compounds H-H+, He+-He2+ and He+-H+ are analyzed. For this purpose, the two-center Dirac equation is solved by a dual-kinetic balanced finite-basis-set method for axially symmetric systems, called A-DKB. This method allows for a complete relativistic consideration of the electron at fixed inter-nuclear distances. A comparison of the obtained results with the nonrelativistic and relativistic calculations presented in the literature is performed. Without pursuing the goal of high accuracy calculations, the advantages and disadvantages of the approach, as well as possible applications of the method, are discussed in detail.

Selection and Validation of qRT-PCR Internal Reference Genes to Study Flower Color Formation in Camellia impressinervis.

Real-time quantitative PCR (qRT-PCR) is a pivotal technique for gene expression analysis. To ensure reliable and accurate results, the internal reference genes must exhibit stable expression across varied experimental conditions. Currently, no internal reference genes for Camellia impressinervis have been established. This study aimed to identify stable internal reference genes from eight candidates derived from different developmental stages of C. impressinervis flowers. We employed geNorm, NormFinder, and BestKeeper to evaluate the expression stability of these candidates, which was followed by a comprehensive stability analysis. The results indicated that CiTUB, a tubulin gene, exhibited the most stable expression among the eight reference gene candidates in the petals. Subsequently, CiTUB was utilized as an internal reference for the qRT-PCR analysis of six genes implicated in the petal pigment synthesis pathway of C. impressinervis. The qRT-PCR results were corroborated by transcriptome sequencing data, affirming the stability and suitability of CiTUB as a reference gene. This study marks the first identification of stable internal reference genes within the entire genome of C. impressinervis, establishing a foundation for future gene expression and functional studies. Identifying such stable reference genes is crucial for advancing molecular research on C. impressinervis.

Lineage-level species distribution model to assess the impact of climate change on the habitat suitability of Boleophthalmus pectinirostris

Global climate change has profound impacts on the habitats of marine organisms, and predicting the habitat changes of species under climate change conditions is crucial for species sustainability. Boleophthalmus pectinirostris is an intertidal fish species that holds significant ecological and economic value. To better protect and manage its resources, this study aimed to predict its current potential distribution and habitat changes under different climate scenarios in the future. This study firstly quantified the hypervolume niches of the three lineages (AE1, AE2, and AES lineages) and compared the niche differentiation among them. Furthermore, this study constructed species-level and lineage-level species distribution models (SDMs) to assess the impact of climate change on the habitat suitability of B. pectinirostris. The result of the niche differentiation assessment showed that there was marked differentiation in niches among the three lineages. The responses of different lineages to environmental variables were different, suggesting that lineage-level models may provide more accurate prediction results. According to the model predictions, the AES may have greater resilience to climate change and may experience habitat expansion in the future, while the AE1 and the AE2 may face habitat loss in some regions. Climate change-driven shifts in oceanic conditions were anticipated to affect the distribution and community structure of marine organisms. This study assessed the impact of climate change on the suitable habitat range of three lineages of B. pectinirostris using SDMs. Consistent with previous studies, the results of our study indicated that lineage-level SDMs may be more reliable than species-level SDMs for species with population differentiation in terms of the accuracy of predictions. In addition, considering the vulnerability of the AE1 and AE2 lineages to climate change, conserving these two lineages should be given a higher priority. The results of this study will provide important information for the future management and conservation of this species.

ENHANCING TONGUE REGION SEGMENTATION THROUGH SELF-ATTENTION AND TRANSFORMER BASED

As an essential component of traditional Chinese medicine diagnosis, tongue diagnosis has faced limitations in clinical practice due to its subjectivity and reliance on the experience of physicians. Recent advancements in deep learning techniques have opened new possibilities for the automated analysis and diagnosis of tongue images. In this paper, we collected 500 tongue images from various patients. These images were initially preprocessed and annotated, resulting in the dataset used for this experiment. This project is based on the previously proposed segmentation method using Harnessing Self-Attention and Transformer, which is divided into three key stages: feature extraction, feature fusion, and segmentation prediction. By organically combining these three key stages, our tongue region segmentation model is better equipped to handle complex tongue images and provides accurate segmentation results. The segmentation DICE coefficient reaches 0.953, which is of significant importance for the automation and objectivity of tongue diagnosis in traditional Chinese medicine.

A new method for analysis of marine sustainable energy systems

This paper proposes to utilise a new adaptive kernel density estimation (KDE) methodology based on linear diffusion processes for predicting the probability distribution tails of sea-state parameters. A key conclusion was reached that the proposed new methodology can lead to more accurate prediction results than traditional methods based on fittings to a measured significant wave height data set at National Data Buoy Center station 46014. This proposed methodology was subsequently utilised for deriving an accurate 50-year environmental contour line that was used in the dynamic analysis of a two-body point absorber wave energy converter. After systematically analysing the calculation results, another key conclusion was drawn that it is advantageous to use a more reliable contour line derived using the proposed new methodology for long-term dynamic analysis of wave energy converters. In summary, the proposed new adaptive KDE methodology is recommended to be utilised and to be refined continuously in future research work in the field of long-term reliability analysis of marine sustainable energy systems.

On logistic regression versus support vectors machine using vaccination dataset

The performance of two classification techniques, logistic regression and Support Vector Machines (SVMs), in assessing vaccination data is investigated in this study. The model was trained based on leave-out-one cross validation to obtain an accurate result. Simulated with ten thousand replications, a life data set was used to establish a better model. The findings from the simulation revealed that the logistic regression model slightly outperformed the SVM while the life data shows that the tuned SVM outperformed both the logistic and the SVM. This demonstrates the practical utility of advanced approaches such as SVMs in difficult categorization scenarios such as vaccination prediction. The study emphasizes the superiority of the customized SVM model in this setting, as well as the potential of machine learning approaches to increase comprehension of complicated healthcare scenarios and guide data-driven decision-making for influencing vaccination plans and public health. The study recommends the use of logistic regression if the data point is high.

A semi-analytical procedure to determine the ion recombination correction factor in high dose-per-pulse beams.

The conventional theories and methods of determining the ion recombination correction factor, such as Boag theory and the related two voltage method and Jaffé plot extrapolation, do not seem to yield accurate results in FLASH /high dose per pulse (DPP) beams ( 10 mGy DPP). This is due to the presence of a large free electron fraction that distorts the electric field inside the chamber sensitive volume. To understand the influence of these effects on the ion recombination correction factor and to develop new expressions for it, it is necessary to re-visit the underlyingphysics. To present a mathematical procedure to develop an analytical expression for the ion recombination correction factor. The expression is the basis for an extrapolation method so the correction factor can be determined in a clinical setting. A semi-analytical solution method, the homotopy perturbation method (HPM), is used to solve the partial differential equations(PDEs) describing the charge carrier physics, including space charge and free electrons. The electron velocity and attachment rate are modeled as functions of the electric field strength. An expression for the charge collection efficiency and ion recombination correction factor are developed. A fit procedure based on this expression is used to compare it to measured data from previously published articles. Another fit procedure using a general equationis also proposed and compared to the data. The series obtained for the charge collection efficiency and the ion recombination correction factor are determined to be asymptotic series and the optimal truncation established. The ion recombination correction factor exhibits a dependency due to the free electron presence. The fit using this expression agrees well with measured data as long as (1) the DPP is below 1 Gy for chambers with a 1 mm plate separation and (2) when the DPP is below 3 Gy for chambers with a 0.5 mm plate separation. In these DPP ranges, the deviation between measured and fit value did not exceed 6%. In both chamber cases the voltage range where the fit applies decreases as DPP increases. The general equationyielded comparable results. The HPM was shown to be applicable to a complex system of PDEs and generate meaningful and novel solutions, as they include both space charge and free electrons. The HPM also lends itself to other chamber geometries. The fit procedure was also shown to yield accurate results for the ion recombination correction up to the 1 Gy DPPlevel.

Closing the gap: Oxford Nanopore Technologies R10 sequencing allows comparable results to Illumina sequencing for SNP-based outbreak investigation of bacterial pathogens.

Whole-genome sequencing has become the method of choice for bacterial outbreak investigation, with most clinical and public health laboratories currently routinely using short-read Illumina sequencing. Recently, long-read Oxford Nanopore Technologies (ONT) sequencing has gained prominence and may offer advantages over short-read sequencing, particularly with the recent introduction of the R10 chemistry, which promises much lower error rates than the R9 chemistry. However, limited information is available on its performance for bacterial single-nucleotide polymorphism (SNP)-based outbreak investigation. We present an open-source workflow, Prokaryotic Awesome variant Calling Utility (PACU) (https://github.com/BioinformaticsPlatformWIV-ISP/PACU), for constructing SNP phylogenies using Illumina and/or ONT R9/R10 sequencing data. The workflow was evaluated using outbreak data sets of Shiga toxin-producing Escherichia coli and Listeria monocytogenes by comparing ONT R9 and R10 with Illumina data. The performance of each sequencing technology was evaluated not only separately but also by integrating samples sequenced by different technologies/chemistries into the same phylogenomic analysis. Additionally, the minimum sequencing time required to obtain accurate phylogenetic results using nanopore sequencing was evaluated. PACU allowed accurate identification of outbreak clusters for both species using all technologies/chemistries, but ONT R9 results deviated slightly more from the Illumina results. ONT R10 results showed trends very similar to Illumina, and we found that integrating data sets sequenced by either Illumina or ONT R10 for different isolates into the same analysis produced stable and highly accurate phylogenomic results. The resulting phylogenies for these two outbreaks stabilized after ~20 hours of sequencing for ONT R9 and ~8 hours for ONT R10. This study provides a proof of concept for using ONT R10, either in isolation or in combination with Illumina, for rapid and accurate bacterial SNP-based outbreak investigation.

An intelligent prediction model of epidemic characters based on multi‐feature

AbstractThe epidemic characters of Omicron (e.g. large‐scale transmission) are significantly different from the initial variants of COVID‐19. The data generated by large‐scale transmission is important to predict the trend of epidemic characters. However, the results of current prediction models are inaccurate since they are not closely combined with the actual situation of Omicron transmission. In consequence, these inaccurate results have negative impacts on the process of the manufacturing and the service industry, for example, the production of masks and the recovery of the tourism industry. The authors have studied the epidemic characters in two ways, that is, investigation and prediction. First, a large amount of data is collected by utilising the Baidu index and conduct questionnaire survey concerning epidemic characters. Second, the β‐SEIDR model is established, where the population is classified as Susceptible, Exposed, Infected, Dead and β‐Recovered persons, to intelligently predict the epidemic characters of COVID‐19. Note that β‐Recovered persons denote that the Recovered persons may become Susceptible persons with probability β. The simulation results show that the model can accurately predict the epidemic characters.