Prediction of Damping Capacity Demand in Seismic Base Isolators via Machine Learning

Base isolators used in buildings provide both a good acceleration reduction and structural vibration control structures.The base isolators may lose their damping capacity over time due to environmental or dynamic effects.This deterioration of them requires the determination of the maintenance and repair needs and is important for the long-termisolator life.In this study,an artificial intelligence prediction model has been developed to determine the damage and maintenance-repair requirements of isolators as a result of environmental effects and dynamic factors over time.With the developed model,the required damping capacity of the isolator structure was estimated and compared with the previously placed isolator capacity,and the decrease in the damping property was tried to be determined.For this purpose,a data set was created by collecting the behavior of structures with single degrees of freedom(SDOF),different stiffness,damping ratio and natural period isolated from the foundation under far fault earthquakes.The data is divided into 5 different damping classes varying between 10%and 50%.Machine learning model was trained in damping classes with the data on the structure’s response to random seismic vibrations.As a result of the isolator behavior under randomly selected earthquakes,the recorded motion and structural acceleration of the structure against any seismic vibration were examined,and the decrease in the damping capacity was estimated on a class basis.The performance loss of the isolators,which are separated according to their damping properties,has been tried to be determined,and the reductions in the amounts to be taken into account have been determined by class.In the developed prediction model,using various supervised machine learning classification algorithms,the classification algorithm providing the highest precision for the model has been decided.When the results are examined,it has been determined that the damping of the isolator structure with the machine learning method is predicted successfully at a level exceeding 96%,and it is an effective method in deciding whether there is a decrease in the damping capacity.

Role of micro-Ca/In alloying in tailoring the microstructural characteristics and discharge performance of dilute Mg-Bi-Sn-based alloys as anodes for Mg-air batteries

The influence of micro-Ca/In alloying on the microstructural charac teristics,electrochemical behaviors and discharge properties of extruded dilute Mg-0.5Bi-0.5Sn-based(wt.%)alloys as anodes for Mg-air batteries are evaluated.The grain size and texture intensity of the Mg-Bi-Sn-based alloys are significantly decreased after the Ca/In alloying,particularly for the In-containing alloy.Note that,in addition to nanoscale Mg_(3)Bi_(2)phase,a new microscale Mg_(2)Bi_(2)Ca phase forms in the Ca-containing alloy.The electrochemical test results demonstrate that Ca/In micro-alloying can enhance the electrochemical activity.Using In to alloy the Mg-Bi-Sn-based alloy is effective in restricting the cathodic hydrogen evolution(CHE)kinetics,leading to a low self-corrosion rate,while severe CHE occurred after Ca alloying.The micro-alloying of Ca/In to Mg-Bi-Sn-based alloy strongly deteriorates the compactness of discharge products film and mitigates the"chunk effect"(CE),hence the cell voltage,anodic efficiency as well as discharge capacity are greatly improved.The In-containing alloy exhibits outstanding discharge performance under the combined effect of the modified microstructure and discharge products,thus making it a potential anode material for primary Mg-air battery.

Laboratory Evaluation of Performance Recovery of Aged SBS-modified Asphalt Using Regenerants with Different Base Oil Components

In this study,the regenerative effects of different regenerants on aged SBS-modified asphalt from different perspectives were investigated,including their conventional properties,viscoelastic behavior,creep-related properties,and micromorphology.Base oils composed of different proportions of aromatic and saturated hydrocarbons as well as the styrene-butadiene-styrene(SBS)restorer were used to prepare the regenerants.The results showed that the components of the base oil of the regenerant played a crucial role in determining the characteristics and performance of the recycled SBSmodified asphalt.Regenerants containing a high proportion of aromatics dissolved the hard segment in the SBS restorer,thereby delaying the effect of a reduction in the regenerants on the performance of the aged asphalts at a high temperature.Regenerants containing a high proportion of saturates dissolved the soft segment in the SBS restorer to enhance the lowtemperature performance of the recycled asphalts.In addition,the stress sensitivity of the recycled asphalts increased with the fraction of aromatics in the regenerant.As the aromatic content of the base oil components of the regenerants increased and their saturate content decreased,the state of dispersion of the SBS phase in the recycled SBS-modified asphalts improved.The optimal content of aromatics in the base oil of the regenerants should be set in the range of 33%to 47%to ensure the adequate performance of the recycled asphalts and a high efficiency of the SBS restorer.

Prediction of Lubricant Physicochemical Properties Based on Gaussian Copula Data Expansion

The composition of base oils affects the performance of lubricants made from them.This paper proposes a hybrid model based on gradient-boosted decision tree(GBDT)to analyze the effect of different ratios of KN4010,PAO40,and PriEco3000 component in a composite base oil system on the performance of lubricants.The study was conducted under small laboratory sample conditions,and a data expansion method using the Gaussian Copula function was proposed to improve the prediction ability of the hybrid model.The study also compared four optimization algorithms,sticky mushroom algorithm(SMA),genetic algorithm(GA),whale optimization algorithm(WOA),and seagull optimization algorithm(SOA),to predict the kinematic viscosity at 40℃,kinematic viscosity at 100℃,viscosity index,and oxidation induction time performance of the lubricant.The results showed that the Gaussian Copula function data expansion method improved the prediction ability of the hybrid model in the case of small samples.The SOA-GBDT hybrid model had the fastest convergence speed for the samples and the best prediction effect,with determination coefficients(R^(2))for the four indicators of lubricants reaching 0.98,0.99,0.96 and 0.96,respectively.Thus,this model can significantly reduce the model’s prediction error and has good prediction ability.

RoBGP:A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer

Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and categorize them into predefined entity types.This process can provide basic support for the automatic construction of knowledge bases.In contrast to general texts,biomedical texts frequently contain numerous nested entities and local dependencies among these entities,presenting significant challenges to prevailing NER models.To address these issues,we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer(RoBGP).Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors.It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information,effectively addressing the issue of long-distance dependencies.Furthermore,the Global Pointer model is employed to comprehensively recognize all nested entities in the text.We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models.This research confirms the effectiveness of RoBGP in Chinese biomedical NER,providing reliable technical support for biomedical information extraction and knowledge base construction.

Buckling Optimization of Curved Grid Stiffeners through the Level Set Based Density Method

Stiffened structures have great potential for improvingmechanical performance,and the study of their stability is of great interest.In this paper,the optimization of the critical buckling load factor for curved grid stiffeners is solved by using the level set based density method,where the shape and cross section(including thickness and width)of the stiffeners can be optimized simultaneously.The grid stiffeners are a combination ofmany single stiffenerswhich are projected by the corresponding level set functions.The thickness and width of each stiffener are designed to be independent variables in the projection applied to each level set function.Besides,the path of each single stiffener is described by the zero iso-contour of the level set function.All the single stiffeners are combined together by using the p-norm method to obtain the stiffener grid.The proposed method is validated by several numerical examples to optimize the critical buckling load factor.

Simulation of Haloxylon Ammodendron Stand Basic Diameter Structure Based on Fuzzy Distribution Function

Based on the investigation data of 12 Haloxylon ammodendron plots in the south edge of Gurbantunggut Desert, Fuzzy distribution was introduced into the study of Haloxylon ammodendron base diameter structure fitting according to the consistency between the characteristics of Fuzzy distribution function and the distribution series of cumulative percentage of stand base diameter, and the fitting precision and effect of Fuzzy distribution function were discussed. The root mean square error RMSE and determination coefficient R2 values showed that Fuzzy-Γ1, Fuzzy-Γ2, Fuzzy-Γ3, Fuzzy-Γ4 had good fitting performance, among which Fuzzy-Γ1 had relatively high fitting precision, and its parameters were closely related to stand age and density, Fuzzy-Γ2 distribution function was the second, and Fuzzy-Γ4 distribution function had the worst fitting effect. By introducing a parameter c from the similarity of four distribution function formulas, a generalized Fuzzy distribution function Fuzzy-Γ5 is obtained. This function shows the highest fitting accuracy. Most of the values of parameter c are near 1 or 2, which shows that the diameter distribution is mainly approximate to Fuzzy-Γ1 and Fuzzy-Γ2.

Are case reports valuable?Exploring their role in evidence based medicine and patient care

Case reports,often overlooked in evidence-based medicine(EBM),play a pivotal role in healthcare research.They provide unique insights into rare conditions,novel treatments,and adverse effects,serving as valuable educational tools and generating new hypothesis.Despite their limitations in generalizability,case reports contribute significantly to evidence-based practice by offering detailed clinical information and fostering critical thinking among healthcare professionals.By acknowledging their limitations and adhering to reporting guidelines,case reports can contribute significantly to medical knowledge and patient care within the evolving landscape of EBM.This editorial explores the intrinsic value of case reports in EBM and patient care.

Layer by Layer Self-assembly Fiber-based Flexible Electrochemical Transistor

Poly(3,4-ethylenedioxyethiophene)-polystyrene sulfonic acid(PEDOT:PSS)/polyallyl dimethyl ammonium chloride modified reduced graphene oxide(PDDA-rGO)was layer by layer self-assembled on the cotton fiber.The surface morphology and electric property was investigated.The results confirmed the dense membrane of PEDOT:PSS and the lamellar structure of PDDA-rGO on the fibers.It has excellent electrical conductivity and mechanical properties.The fiber based electrochemical transistor(FECTs)prepared by the composite conductive fiber has a maximum output current of 8.7 mA,a transconductance peak of 10 mS,an on time of 1.37 s,an off time of 1.6 s and excellent switching stability.Most importantly,the devices by layer by layer self-assembly technology opens a path for the true integration of organic electronics with traditional textile technologies and materials,laying the foundation for their later widespread application.

The Impact of Model Based Offset Scaling Technique on the Amplitude Variation with Offset Responses from 3D Seismic Data Acquired from the Tano Basin, Offshore Ghana

Amplitudes have been found to be a function of incident angle and offset. Hence data required to test for amplitude variation with angle or offset needs to have its amplitudes for all offsets preserved and not stacked. Amplitude Variation with Offset (AVO)/Amplitude Variation with Angle (AVA) is necessary to account for information in the offset/angle parameter (mode converted S-wave and P-wave velocities). Since amplitudes are a function of the converted S- and P-waves, it is important to investigate the dependence of amplitudes on the elastic (P- and S-waves) parameters from the seismic data. By modelling these effects for different reservoir fluids via fluid substitution, various AVO geobody classes present along the well and in the entire seismic cube can be observed. AVO analysis was performed on one test well (Well_1) and 3D pre-stack angle gathers from the Tano Basin. The analysis involves creating a synthetic model to infer the effect of offset scaling techniques on amplitude responses in the Tano basin as compared to the effect of unscaled seismic data. The spectral balance process was performed to match the amplitude spectra of all angle stacks to that of the mid (26°) stack on the test lines. The process had an effect primarily on the far (34° - 40°) stacks. The frequency content of these stacks slightly increased to match that of the near and mid stacks. In offset scaling process, the root mean square (RMS) amplitude comparison between the synthetic and seismic suggests that the amplitude of the far traces should be reduced relative to the nears by up to 16%. However, the exact scaler values depend on the time window considered. This suggests that the amplitude scaling with offset delivered from seismic processing is only approximately correct and needs to be checked with well synthetics and adjusted accordingly prior to use for AVO studies. The AVO attribute volumes generated were better at resolving anomalies on spectrally balanced and offset scaled data than data delivered from conventional processing. A typical class II AVO anomaly is seen along the test well from the cross-plot analysis and AVO attribute cube which indicates an oil filled reservoir.

An Improved Bounded Conflict-Based Search for Multi-AGV Pathfinding in Automated Container Terminals

As the number of automated guided vehicles(AGVs)within automated container terminals(ACT)continues to rise,conflicts have becomemore frequent.Addressing point and edge conflicts ofAGVs,amulti-AGVconflict-free path planning model has been formulated to minimize the total path length of AGVs between shore bridges and yards.For larger terminalmaps and complex environments,the grid method is employed to model AGVs’road networks.An improved bounded conflict-based search(IBCBS)algorithmtailored to ACT is proposed,leveraging the binary tree principle to resolve conflicts and employing focal search to expand the search range.Comparative experiments involving 60 AGVs indicate a reduction in computing time by 37.397%to 64.06%while maintaining the over cost within 1.019%.Numerical experiments validate the proposed algorithm’s efficacy in enhancing efficiency and ensuring solution quality.

Imaging characteristics and treatment strategies for carotid artery occlusion caused by skull base fracture:Three case reports

BACKGROUND Traumatic internal carotid artery(ICA)occlusion is a rare complication of skull base fractures,characterized by high mortality and disability rates,and poor prognosis.Therefore,timely discovery and correct management are crucial for saving the lives of such patients and improving their prognosis.This article retrospectively analyzed the imaging and clinical data of three patients,to explore the imaging characteristics and treatment strategies for carotid artery occlusion,combined with severe skull base fractures.CASE SUMMARY This case included three patients,all male,aged 21,63,and 16 years.They underwent plain film skull computed tomography(CT)examination at the onset of their illnesses,which revealed fractures at the bases of their skulls.Ultimately,these cases were definitively diagnosed through CT angiography(CTA)examinations.The first patient did not receive surgical treatment,only anticoagulation therapy,and recovered smoothly with no residual limb dysfunction(Case 1).The other two patients both developed intracranial hypertension and underwent decompressive craniectomy.One of these patients had high intracranial pressure and significant brain swelling postoperatively,leading the family to choose to take him home(Case 2).The other patient also underwent decompressive craniectomy and recovered well postoperatively with only mild limb motor dysfunction(Case 3).We retrieved literature from PubMed on skull base fractures causing ICA occlusion to determine the imaging characteristics and treatment strategies for this type of disease.CONCLUSION For patients with cranial trauma combined with skull base fractures,it is essential to complete a CTA examination as soon as possible,to screen for blunt cerebrovascular injury.

Influences of Skin and Base Types on the Soothing Assessment of Cosmetic Products

In order to assess the responses of different skin types stimulated by tape stripping,after which two kinds of brand cosmetics of cream and hydrogel,were separately applied to evaluate the individual soothing effect of the product on different skin types.A total of 30 volunteers aged 18~65 years were recruited,which were divided into four groups(oily,dry,normal and combined)according to the self-oiling ability of facial and T zones.Four 3 cm×3 cm of skins on inner forearm of each volunteer were chosen and randomly marked as blank,negative control,soothing cream and soothing gel.The blank area was kept untreated,and the remaining areas were tape-stripped by 10 times(Transparent universal 1527C-1,3M China Co.,Ltd.,)to disrupt the skin corneum and induce erythema.Images,erythema index(EI)and transcutaneous water loss rate(TEWL)of the marked skin areas before and 5 min after tape-stripping were taken to assess the distinct responses of different skin type to the taping treatment;Ten minutes after tape-stripping,The soothing products were applied to the test area,the skin images,EI and TEWL at 20 min and 50 min(corresponding to 30 min and 60 min after tape-stripping,respectively)were measured after cosmetics application to compare and evaluate the soothing effects of cream and hydrogel on different skin types.Results showed that different skin displayed diverse reactions to the tape-stripping,and the oily skin was more sensitive with longer duration of erythema than other skin types.After the application of soothing products,both cream and hydrogel could relieve skin erythema,while the soothing effects are dependent both on skin types and the cosmetic base materials:Hydrogel has a better efficacy in alleviating erythema and TEWL of dry skin,while the cream was more effective on normal,oily and combined skin types.The results suggest that the modelling method for soothing evaluation should consider the skin types of the volunteers,and the cosmetics of different base materials might have the target skin types.

A Study on the Explainability of Thyroid Cancer Prediction:SHAP Values and Association-Rule Based Feature Integration Framework

In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable progress.Accurate predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce overtreatment.However,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency.This paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present state-of-the-artmodels.Our study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction models.In the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the dataset.The original dataset is used in trainingmachine learning models,and further used in generating SHAP values fromthesemodels.In the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based analysis.This new integrated dataset is used in re-training the machine learning models.The new SHAP values generated from these models help in validating the contributions of feature sets in predicting malignancy.The conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making systems.In this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the predictions.The study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of explainability.The proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1-score,and the area under the receiver operating characteristic(AUROC)are also higher than the baseline models.The results of the proposed model help us identify the dominant feature sets that impact thyroid cancer classification and prediction.The features{calcification}and{shape}consistently emerged as the top-ranked features associated with thyroid malignancy,in both association-rule based interestingnessmetric values and SHAPmethods.The paper highlights the potential of the rule-based integrated models with SHAP in bridging the gap between the machine learning predictions and the interpretability of this prediction which is required for real-world medical applications.

Investigating the Impact of Base Heating and External Electric Field on PV Cell Performance under Intense Illumination

The characterization of the performances of a PV cell is linked to intrinsic factors of this cell. It is therefore important for us to identify the favorable or unfavorable conditions that affect the performance of PV cells. It is from this perspective that it seems judicious to us to study the simultaneous influence of the heating of the base and an external electric field on the performance of a PV cell under intense illumination of 50 suns. Two phenomena contribute to the heating of the base of a PV cell which is heating due to the transfer by conduction of solar radiation energy received by the surface of the PV cell and the heat generated inside the PV cell by various phenomena linked to the movement of photogenerated charged carriers. In this study, we take into account the heating linked to the movement of the charged carriers in the base. After a mathematical modeling of the PV cell considered, some hypotheses are formulated and the expressions of the electrical parameters are established as a function of the electric field and base temperature. Subsequently, we use numerical simulation to highlight the behavior of theses parameters as a function of temperature and of the intensity of the electric field. The results show that for any given temperature, the orientation of the electric field as considered in our work improves the performance of the PV cell while high temperatures degrade these performances. Furthermore, the analysis of the curves shows that the harmful effect of temperature on the performance of a PV cell is more accentuated at large values of electric field.

Modelling of the variation of granular base materials resilient modulus with material characteristics and humidity conditions

This study aims to quantify the susceptibility of granular materials used in pavements to changes in moisture content and propose a correlation model to incorporate this susceptibility into seasonal analyses.The fines content and the percentage of fractured coarse aggregates were identified as direct indicators of the resilient modulus susceptibility to changes in water content.The results showed that the percentage of fractured coarse aggregates particles(FR)has a more significant impact on the resilient modulus(Er)of crushed granular materials used in pavement construction than the combined indicator of the fines content and sample volumetrics(nf).Crushed granular materials with a higher percentage of fractured coarse aggregates are relatively insensitive to changes in the degree of saturation,but become more sensitive as the fine fraction porosity decreases.An adjusted model was proposed based on the existing formulation,but considers a complex parameter to describe and adjust the sensitivity of base granular materials to variations in moisture content with respect to fabrication charac-teristics,fines content and volumetric properties.The model shows that the variation of Er values is below10%for fully crushed granular materials.However,it reaches approximately±12%for materials with 75%of crushed coarse aggregates andþ40%and-25%for materials with FR=50%.This model could help select good ag-gregates characteristics and adjust grain-size distribution for environments where significant moisture content variations can occur in the pavement system,such as in the Province of Quebec(Canada).As it is based on pa-rameters that can be easily determined or estimated,it also represents a valuable tool for detailed design and analysis that can consider material characteristics.

Non-Recursive Base Conversion Using a Deterministic Markov Process

We prove that non-recursive base conversion can always be implemented by using a deterministic Markov process. Our paper discusses the pros and cons of recursive and non-recursive methods, in general. And we include a comparison between non-recursion and a deterministic Markov process, proving that the Markov process is twice as efficient.

Synthesis and Characterization of Novel Schiff Base for Enhanced Dye-Sensitized Solar Cell Photo-Response Mechanism

The efficient photo-response mechanism is one of the key factors in the commercialization of dye-sensitized solar cells in a bid to satisfy renewable energy demands. Progress in green technology has put solar energy on the front burner as a provider of clean and affordable energy for a sustainable society. We report the synthesis of a novel Schiff base with optical transparency in the visible and near IR region of the solar spectrum that can find application in the DSSCs photo-response mechanism. The synthesized crystal exhibited features that could handle some of the shortcomings of dye-sensitized solar cells which include wide band solar spectrum absorption and capability for swift charge transfer within the photoelectrodes. The synthesized Schiff base was characterized using x-ray diffractometer, UV/Visible spectrometer, Frontier transmission infrared spectrometer and conductometer. XRD data revealed the grown crystal to have an average crystallite size of 2.08 nm with average microstrain value of about 269.43. The FT-IR recorded transmission wave ѵ (CO) at 1207.7 cm−1 while dominant wave occurred at ѵ1654.9 and ѵ1592.3 cm−1 relating to ѵ (CN) stretching and ѵ (NH) bending respectively were observed. The IR spectrum revealed the bonding species and a probable molecular structure of 2,6-bis(benzyloxy)pyridine. The UV/Visible spectra convoluted to maximum peak within the near IR region suggesting that 2,6-bis(benzyloxy)pyridine can absorb both the visible and near IR region while its electrical conductivity was determined to be 4.58 µS/cm. The obtained result of the present study revealed promising characteristics of a photosensitizer that can find application in the photo-response mechanism of DSSCs.

Effective Utilization of Government-Provided CTI by Small Businesses within the Defense Industrial Base

There are two broad objectives of the research reported in this paper. First, we assess whether government-provided cyber threat intelligence (CTI) is helpful in preventing, or responding to, cyber-attacks among small businesses within the U.S. Defense Industrial Base (DIB). Second, we identify ways of improving the effectiveness of government-provided CTI to small businesses within the DIB. Based on a questionnaire-based survey, our findings suggest that government-provided CTI helps businesses within the DIB in preventing, or responding to, cyber-attacks providing a firm is familiar with the CTI. Unfortunately, a large percentage of small firms are not familiar with the government-provided CTI feeds and consequently are not utilizing the CTI. This latter situation is largely due to financial constraints confronting small businesses that prevent firms from having the wherewithal necessary to effectively utilize the government-provided CTI. However, we found a significant positive association between a firm’s familiarity with the government-provided CTI and whether a firm is being periodically reviewed by the Defense Counterintelligence and Security Agency (DCSA) or is compliant with the Cybersecurity Maturity Model Certification (CMMC) program. The findings from our study also show that the participating firms believe that external cyber threats are more likely to be the cause of a future cybersecurity breach than internal cybersecurity threats. Finally, our study found that the portion of the IT budget that small businesses within the DIB spend on cybersecurity-related activities is dependent on the perception that a firm would be the target of an external cyber-attack.

A Health State Prediction Model Based on Belief Rule Base and LSTM for Complex Systems

In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling structures,non-linear and time-varying characteristics,so it is a challenge to establish a reliable prediction model.The belief rule base(BRB)can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities.Since each indicator of the complex system can reflect the health state to some extent,the BRB is built based on the causal relationship between system indicators and the health state to achieve the prediction.A health state prediction model based on BRB and long short term memory for complex systems is proposed in this paper.Firstly,the LSTMis introduced to predict the trend of the indicators in the system.Secondly,the Density Peak Clustering(DPC)algorithmis used todetermine referential values of indicators for BRB,which effectively offset the lack of expert knowledge.Then,the predicted values and expert knowledge are fused to construct BRB to predict the health state of the systems by inference.Finally,the effectiveness of the model is verified by a case study of a certain vehicle hydraulic pump.