Static Model Research Articles

Design and optimization of a planar anti-buckling compliant rotational joint with a remote center of motion

Compliant mechanisms (CMs) exhibit some excellent mechanical properties and provide numerous innovative solutions for many existing mechanical applications. Among them, planar compliant rotational joints have made substantial contributions to precision engineering. To achieve high-precision positioning with a compliant rotational joint, it is essential to study characteristics such as anti-buckling and constant stiffness. The remote center of motion (RCM) mechanism, with its compact structure and ease of precise control, is expected to enhance overall rigidity and reduce parasitic displacements. Here, we propose a planar anti-buckling compliant rotational joint with a RCM. Static modeling and model validation are conducted for different geometric configurations, including single and combined structures. A distributed configuration with bidirectional anti-buckling properties is selected for optimization. Using a global parameter optimization model, single-objective optimization studies are conducted for three distinct characteristics. Subsequently, a multi-objective optimization model for constant stiffness and high precision is established. Based on the optimization results, a compliant rotational joint with bidirectional anti-buckling, constant rotational stiffness, and high precision is designed. Finally, the effectiveness of the optimization method is validated through physical experiments.

Dynamic location model for designated COVID-19 hospitals in China.

In order to effectively cope with the situation caused by the COVID-19 pandemic, cases should be concentrated in designated medical institutions with full capability to deal with patients infected by this virus. We studied the location of such hospitals dividing the patients into two categories: ordinary and severe. Genetic algorithms were constructed to achieve a three-phase dynamic approach for the location of hospitals designated to receive and treat COVID-19 cases based on the goal of minimizing the cost of construction and operation isolation wards as well as the transportation costs involved. A dynamic location model was established with the decision variables of the corresponding 'chromosome' of the genetic algorithms designed so that this goal could be reached. In the static location model, 15 hospitals were required throughout the treatment cycle, whereas the dynamic location model found a requirement of only 11 hospitals. It further showed that hospital construction costs can be reduced by approximately 13.7% and operational costs by approximately 26.7%. A comparison of the genetic algorithm and the Gurobi optimizer gave the genetic algorithm several advantages, such as great convergence and high operational efficiency.

Antimicrobial and antibiofilm activity of human recombinant H1 histones against bacterial infections.

Histones possess significant antimicrobial potential, yet their activity against biofilms remains underexplored. Moreover, concerns regarding adverse effects limit their clinical implementation. We investigated the antibacterial efficacy of human recombinant histone H1 subtypes against Pseudomonas aeruginosa PAO1, both planktonic and in biofilms. After the in vitro tests, toxicity and efficacy were assessed in a P. aeruginosa PAO1 infection model using Galleria mellonella larvae. Histones were also evaluated in combination with ciprofloxacin (Cpx) and gentamicin (Gm). Our results demonstrate antimicrobial activity of all three histones against P. aeruginosa PAO1, with H1.0 and H1.4 showing efficacy at lower concentrations. The bactericidal effect was associated with a mechanism of membrane disruption. In vitro studies using static and dynamic models showed that H1.4 had antibiofilm potential by reducing cell biomass. Neither H1.0 nor H1.4 showed toxicity in G. mellonella larvae, and both increased larvae survival when infected with P. aeruginosa PAO1. Although in vitro synergism was observed between ciprofloxacin and H1.0, no improvement over the antibiotic alone was noted in vivo. Differences in antibacterial and antibiofilm activity were attributed to sequence and structural variations among histone subtypes. Moreover, the efficacy of H1.0 and H1.4 was influenced by the presence and strength of the extracellular matrix. These findings suggest histones hold promise for combating acute and chronic infections caused by pathogens such as P. aeruginosa.IMPORTANCEThe constant increase of multidrug-resistant bacteria is a critical global concern. The inefficacy of current therapies to treat bacterial infections is attributed to multiple mechanisms of resistance, including the capacity to form biofilms. Therefore, the identification of novel and safe therapeutic strategies is imperative. This study confirms the antimicrobial potential of three histone H1 subtypes against both Gram-negative and Gram-positive bacteria. Furthermore, histones H1.0 and H1.4 demonstrated in vivo efficacy without associated toxicity in an acute infection model of Pseudomonas aeruginosa PAO1 in Galleria mellonella larvae. The bactericidal effect of these proteins also resulted in biomass reduction of P. aeruginosa PAO1 biofilms. Given the clinical significance of this opportunistic pathogen, our research provides a comprehensive initial evaluation of the efficacy, toxicity, and mechanism of action of a potential new therapeutic approach against acute and chronic bacterial infections.

Prediction of overweight/obesity risk in adolescence using static and dynamic multi-exposure models

Abstract Background Studying the risk factors for obesity development and timely identification of individuals at increased risk are key for obesity prevention. We aimed to assess the associations of multiple prenatal, infancy and childhood exposures with overweight/obesity at 13 years (y) and develop static and dynamic multiple-exposure prediction models. Methods In 4232 mother-child pairs from the Generation XXI birth cohort, we assessed, during pregnancy/infancy and childhood, 55 sociodemographic factors, anthropometric measures, lifestyle behaviors, and clinical exposures related to the mother and the child and categorized body mass index z-scores as normal weight and overweight/obesity at 13y. Static models for pregnancy/infancy, 4, 7 and 10y, and a dynamic model were fitted using Elastic Net and logistic regression and were compared in terms of predictive performance. Results Mothers with lower educational level and household income, no partner, overweight/obesity before and after pregnancy, excessive gestational weight gain, pregnancy complications and who were smokers during pregnancy had children with a higher risk of overweight/obesity at 13y. Children with higher BMI during infancy, overweight/obesity during childhood, an early introduction of solid foods (before 4 months), a higher consumption of soft drinks at 4y and females had a higher risk of overweight/obesity at 13y. We observed the lowest predictive performance for the pregnancy/infancy model (prediction error of 36%) and the highest predictive performance for the 10y and dynamic models (prediction errors of 17%). Conclusions We identified the most important predictors of overweight/obesity in adolescence, most of them modifiable, and able to be targeted in prevention strategies. Predicting overweight/obesity risk in adolescence might be suboptimal if only considering pregnancy/infancy data and improves as closer to the age of the outcome, with no additional benefit in using a complex dynamic model. Key messages • Prevention of overweight/obesity should start during prenatal life. • Children at higher risk of overweight/obesity may be identified years earlier using prediction models in clinical practice.

Cost-effectiveness of respiratory syncytial virus adjuvanted vaccine in Portuguese aged ≥ 60 years

Abstract Background Respiratory syncytial virus (RSV) infections are one of the leading causes of lower respiratory tract disease among adults aged ≥60 years in Europe. This study aims to assess the potential public health impact of the RSVPreF3 OA adjuvanted vaccine, the first approved preventive intervention for the older adult population in Portugal. Methods A monthly-cycle static Markov model was developed to assess RSVPreF3 OA adjuvanted vaccine impact on adults aged 60 years and older in Portugal. A single dose of RSVPreF3 OA adjuvanted vaccine was compared to no-vaccination over a three-year time-horizon. The model captures lower respiratory tract disease (LRTD) RSV cases, hospitalizations, deaths, and healthcare resource utilization. Data inputs were based on local published literature, supplemented by the best available data when local information was lacking. Results Based on preliminary analysis considering 75% vaccine coverage, model outputs estimate that RSVPreF3 OA adjuvanted vaccine can prevent 71,433 LRTD cases requiring medical attendance, 11,067 hospitalizations, 1,793 intensive care unit admissions and 1,003 deaths. The model estimates that 32 adults need to be vaccinated to avoid one RSV LRTD case, 207 adults need to be vaccinated to avoid one hospitalization and 2,283 adults need to be vaccinated to avoid one death. The incremental cost-effectiveness ratio was estimated at 9,541€, hence the RSVPreF3 OA adjuvanted vaccine is cost-effective compared to no vaccination. Conclusions The results demonstrate a strong preventive impact of the RSVPreF3 OA adjuvanted vaccine on health outcomes and healthcare resource utilization in adults ≥60 years, leading to a significant public health benefit. This result emphasizes the importance of preventing RSV to preserve the health and well-being of Portugal’s older adults. In addition, data from ongoing studies may enhance precision and extensive sensitivity analyses validate the robustness of the model’s input assumptions. Key messages • This study shows the robust evidence of RSVPreF3 OA adjuvanted vaccine to improve public health by preventing thousands of hospitalizations and deaths among ≥60 years, in Portugal. • Vaccinating adults aged ≥ 60 years with RSVPreF3 OA adjuvanted vaccine in Portugal is a cost-effective strategy.

Developing endoscopic cell sheet delivery device using a beating human heart simulator and a pig model for minimally invasive cardiac regenerative therapy

Abstract Introduction Recently, clinical trials have commenced for cardiac regenerative therapy utilizing stem cells in patients with heart failure. Among the various transplantation approaches, there is a growing demand for less invasive methods to transplant sheet/patch-shaped cell products onto the heart surface, rather than resorting to conventional open surgery. This is particularly important considering the need for immunosuppressants due to allogeneic cell transplantation. Objective This study aims to develop a device for endoscopic transplantation of cell sheets onto the beating heart surface. Methods Prototype devices, initially developed in our prior study using a static heart model (Regenerative Therapy, 2020), were refined to better suit the cell sheet transplantation procedure onto a beating heart. We constructed a three-dimensional (3D) model of a beating heart located inside a thoracic cavity model, based on human computed tomography data and a 3D printer dedicated to simulating endoscopic surgical procedures. Human mesenchymal stromal cell (MSC)-derived cell sheets were prepared using temperature-responsive culture dishes and transferred onto a beating heart model pulsating at 80 beats per minute. We assessed the technical feasibility (N=8). Furthermore, skeletal muscle tissue was harvested from the thigh of the Clawn miniature pigs, and skeletal myoblasts were cultured from enzymatically digested tissue to create a cell sheet for autologous transplantation (8.0×10⁶ cells/sheet). Subsequently, the cell sheets labeled with Hoechst 33342 were endoscopically transplanted onto the pig heart surface (N=2). The pigs were sacrificed one week later, and the engraftment of the cell sheet was histologically evaluated. Results Successful deployment of MSC sheets onto the beating heart model was achieved in all procedures with one trial (100% success rate). The procedure duration from device insertion to the completion of cell sheet transplantation averaged 142±19 seconds. In experiments on autologous transplantation of myoblast cell sheets, two cases were successfully accomplished, with the transplanted cell sheet positive for Hoechst 33342 observed on the heart surface. Conclusion We have developed an endoscopic cell sheet delivery device using a pulsating heart model and a pig model, which holds promise for minimally invasive cardiac regenerative therapy in the future. Further investigations into the safety of the procedure are warranted for the clinical application of this system.

Lifelong QoT prediction: an adaptation to real-world optical networks

Predicting the quality of transmission (QoT) is a critical task in the management and optimization of modern fiber-optic networks. Traditional machine learning (ML) QoT prediction models, typically trained on pre-collected datasets, are designed to make long-term predictions once deployed. However, this static training strategy often falls short in the face of time-dependent network evolution and variations. We identify the root cause of these shortcomings as shifts in data distribution, which are not accounted for in conventional static models. In response to these challenges, we propose an online continual learning pipeline that is specifically designed for stable QoT prediction in optical networks. This pipeline directly addresses the problem of distribution shifts by continuously updating the prediction model in response to real-time network data. We explore and compare various strategies within this framework and demonstrate that the integration of the adaptive retraining strategy and the regularized online continual learning algorithm (OCL-REG) significantly enhances the QoT prediction stability while optimizing the resource efficiency. OCL-REG demonstrates superior adaptability and stability, achieving an average cumulative mean squared error (C-MSE) of 0.19 on a testbench with a data distribution shift sequence containing 1000 batches. Moreover, the OCL-REG model requires fewer samples for adaptation, averaging around 107 samples, compared to the conventional retraining strategy, which requires an average of 253 samples. Our approach presents a paradigm shift in QoT prediction, moving from a static to a dynamic, lifelong learning model that is more attuned to the evolving realities of real fiber-optic networks.

Determinants of Inflation Rate Fluctuations in Five ASEAN Nations

This paper examines the key relationships between independent variables and inflation fluctuations in the ASEAN-5 countries, which include Malaysia, Indonesia, Singapore, Thailand, and the Philippines. Using annual data from 2001 to 2020, a static panel data regression model is employed. The empirical results reveal that at least two independent variables significantly impact inflation rates in these five ASEAN nations. Specifically, the unemployment rate has a significant negative correlation with inflation fluctuations, while economic growth shows a significant positive correlation. Overall, this study provides valuable insights into how exchange rate determinants influence inflation fluctuations, highlighting the importance of balanced policies to address the interactions between unemployment, economic growth, and inflation.

The geometry of fault reactivation and uplift along the central part of the Maacama Fault Zone, Northern California Coast Ranges (USA)

Fault reactivation of bedrock structures in active fault zones influences stress state and earthquake rupture phenomena through the introduction of weak slip surfaces that impact fault zone geometry and width. Yet, geometric relationships between modern faults and older reactivated faults are difficult to quantify in rocks that have experienced multiple deformation episodes. We used new geologic mapping, geomorphic tools, and structural modeling to quantify rock uplift and subsurface fault geometry of the central part of the Maacama Fault Zone near Ukiah, California, USA, and the surrounding area. Results suggest that the northern Mayacamas Mountains are in a tectonically driven disequilibrium, with differential rock uplift focused on the western side of the range. Steeply east-dipping fault surfaces and splays characterize the geometry of the Maacama Fault Zone. We mapped two newly identified faults to the east of the main Maacama Fault, the Cow Mountain–Mill Creek Fault, and Willow Creek Fault, which align with a moderately east-dipping cluster of microseismicity between 4–10 km depth beneath the Mayacamas Mountains. Static stress modeling on the Maacama Fault Zone and newly identified faults to the east quantify slip tendency values of 0.5–0.4, which suggests that the faults are moderately to poorly suited for slip in the modern stress field and may be weak. We infer that modern uplift is driven by oblique reverse, up-to-the-east, dip-slip motion on the reactivated Cenozoic Cow Mountain–Mill Creek and Willow Creek Faults as material is advected through a restraining bend on the Maacama Fault. This study shows that reactivated bedrock faults increase the fault zone width and introduce fault surfaces that contribute a component of vertical deformation and uplift in major strike-slip fault zones. Deformation is accommodated on an interconnected network of new and reactivated faults that delineate a complex seismic hazard.

Static and Dynamic Modeling of Non-Performing Loan Determinants in the Eurozone

The issue of non-performing loans (NPLs) in a bank’s portfolio is important for a bank’s stability and sustainability. Their increased presence indicates a potential worsening of the economy and a lower quality of the bank’s assets. We estimated determinants of non-performing loans in the Eurozone for quarterly data 2015–2020. The results confirmed spatial spillover effects within Eurozone countries, which means that when a shock happens in one country in the Eurozone, it will also affect the other economies of the Eurozone area. Based on the Hausman test, a fixed-effects model was chosen as appropriate and showed that bank-specific and macroeconomic determinants significantly affect NPLs in these economies. In relation to previous studies that dealt with this issue, a co-integration analysis was introduced. A significant impact of return on assets, return on equity, and the loan-to-deposit ratio, as well as the gross domestic product, inflation, and exchange rate on NPLs in the short run and long run, was confirmed using a Pooled Mean Group (PMG) estimator. Bank management should customize credit policy based on both internal and external conditions to improve their performance, focusing on enhancing profitability and maintaining a lower loan-to-deposit ratio to reduce NPLs. The research suggests that a higher gross domestic product (GDP) growth rate is associated with fewer NPLs, while inflation uncertainty and a volatile exchange rate can increase NPLs, highlighting the importance of adjusting strategies to the macroeconomic landscape.

Comparative Analysis of the Portfolio-balance Models of Exchange Rate Determination

The current study conducts a comparative analysis of Portfolio-Balance Models (PBM) developed by Branson, Kouri and Dornbusch to assess the role of expectations and time horizons in determining and forecasting the India–US exchange rate over the period 1996:Q2–2019:Q3. Notably, it improves the original models by integrating microstructure theory into their framework. The Autoregressive Distributed Lag Error-Correction Model (ARDL-ECM) is used to investigate both short run and long run behaviour of the models. Additionally, the study assesses out-of-sample forecasting accuracy of the modified models against the Random Walk Model (RWM) using the root mean square error metric. The estimation results reveal that models based on rational expectations are better than the static expectations model. Notably, the microeconomic determinant is counterintuitively significant only in the long run across all models. Furthermore, these modified models demonstrate superior out-of-sample forecasting abilities compared to RWM for alternative forecasting horizons. However, forecasting results over a 6-month period is better with short run models. Over 1-year and 2-year horizons, rational expectations models outperform the static expectations model. This study challenges the Meese–Rogoff puzzle, ensuring that PBM, when modified to incorporate microstructure theory, is valid and yields superior forecasting results compared to RWM. JEL Codes: F31, F32, C22

Dynamic and quasi static stiffness characterization of a lamination stack of an electric motor

The development of electric motors for automotive applications requires precise material models to simulate structural strength and NVH (Noise, Vibration, and Harshness) properties. Modeling the behavior of lamination stacks, composed of stacked steel plates, presents significant challenges. This study conducted dynamic and quasi-static experiments at various preload levels on an unmodified automotive lamination stack. Significant discrepancies were identified between stiffness values obtained from static and dynamic measurements. Consequently, using dynamically obtained stiffness values in static models, and vice versa, leads to inaccuracies and should be avoided. These results enhance the precision and efficiency of simulations used in the design and optimization of electric motors.

A data-driven regression model for predicting thermal plant performance under load fluctuations

The global energy demand is still primarily reliant on fossil-fueled thermal power plants. With the growing share of renewables, these plants must frequently adjust their loads. Maintaining, or ideally increasing operational efficiency under these conditions is crucial. Increasing the efficiency of such systems directly reduces associated greenhouse gas emissions, but it requires sophisticated models and monitoring systems. Data-driven models have proven their value here, as they can be used for monitoring, operational state estimation, and prediction. However, they are also sensitive to (1) the training approach, (2) the selected feature set, (3) and the algorithm used. Using operational data, we comprehensively investigate these model parameters for performance prediction in a thermal plant for process steam generation. Specifically, four regression algorithms are evaluated for the prediction of the highly fluctuating live steam flow with two training approaches and three feature subsets of the raw dataset. Furthermore, manual and automatic clustering methods are used to identify different states of operation regarding the fuel amounts used in the combustion chamber. Our results show that the live steam flow is predicted with excellent accuracy for a testing period of one month (R2=0.994 and NMAE=0.55%) when using a dynamic training approach and a comprehensive feature set comprised of 48 features representing the combustion process. It is also seen that the statically trained model predicts various load changes with strong accuracy and that the accuracy of the dynamically trained model can be approached by incorporating the cluster information into the static model. These models reflect the plant’s physical intricacies under varying loads, where deviations from the predicted live steam flow indicate unwanted long-term drifts. They can be directly implemented to help operators detect inefficiencies and optimize plant performance.

Reflective portfolios: a learning and self-assessment tool

Recent studies have emphasized the growing relevance of portfolios in higher education, particularly as a tool that fosters reflective and formative assessment practices. This article presents a qualitative study conducted with a group of Education students, exploring the portfolio as the primary assessment method. The research investigates the conditions under which portfolios can serve as effective tools for both self and peer assessment, using reflective cognition (metacognition) to enhance learning. Classroom observations were combined with open-ended questionnaires, and data analysis was conducted using webQDA software to ensure rigor and objectivity in interpreting the findings. The study reveals that the use of portfolios not only fostered student agency and autonomy but also strengthened self-responsibility and promoted deeper engagement with the learning process. Furthermore, portfolios facilitated a more dynamic, transparent, and collaborative relationship between students and instructors, offering a structured yet flexible approach to assessment. By aligning with competency-based curricula, the portfolio enables a more personalized and meaningful evaluation of students' development, shifting away from traditional, static assessment models. This study contributes to the advancement of assessment practices in higher education, advocating for portfolios as a critical tool in promoting cognitive development, reflexive thinking, and an integrated approach to learning and assessment.

Reactive scattering of H2 on Cu(111) at 925K: Effective Hartree potential vs sudden approximation.

We present new quantum dynamical results for the reactive scattering of hydrogen molecules from a Cu(111) surface at a surface temperature of 925K. Reaction, scattering, and diffraction probabilities are compared for results obtained using both an effective Hartree potential (EfHP) and a sudden approximation approach, implemented through the static corrugation model (SCM), to include surface temperature effects. Toward this goal, we show how the SRP48 DFT-functional and an embedded atom potential perform when used to calculate copper lattice constants and thermal expansion coefficients based on lattice dynamics calculations within the quasi-harmonic approximation. The so-calculated phonons are then used in the EfHP approach to replace the normal modes of a fictitious copper cluster used in earlier work. We find that both the EfHP and SCM approaches correctly predict the reaction probability curve broadening effect when the surface temperature is increased. Similarly, results for rovibrationally elastic scattering appear to be improved, predominantly for the SCM model. The behavior of the EfHP results appears to remain much closer to that of a Born-Oppenheimer static surface approach, which excludes any surface temperature effects. Finally, for the diffraction, we show very clear attenuation effects for the SCM approach, significantly decreasing specular diffraction probabilities at 925K surface temperature. These results demonstrate that state-of-the-art theoretical models are able to reproduce strictly quantum mechanical scattering effects with a sudden approximation model and open up interesting opportunities for further comparisons to experimental diffraction results.

Smart Building Digital Twin for Interior Water Distribution System Management

Abstract. A smart campus integrates the Internet of Things, artificial intelligence, and real-time sensor data to optimize campus functions and create a context-aware decision-support platform for effective campus management. A crucial aspect of a smart campus is the water distribution system, which faces several challenges due to bursts, leaks, and water quality issues. This study uses Digital Twin technology to address these challenges through real-time monitoring, detection, and management of campus water distribution networks. A building-level digital twin is developed for the interior water networks of the Daphne Cockwell Complex at Toronto Metropolitan University. The major components include a 3D network model capable of analysis and simulations, a smart water management system, and real-time visualization. A 3D static model of the interior water utility network is created using the Industrial Foundation Class data. The semantic and topological models based on graph models are developed for network analysis. The models are integrated into the ESRI ArcGIS Pro to facilitate BIM-GIS integration. A dynamic model is created by incorporating automatic meter readings based on IoT. Combined with the 3D model of the utility network, the digital twin monitors and visualizes building water consumption, facilitating anomaly detection and real-time maintenance by the facilities management department. This study provides practical guidance for managing water distribution systems in university buildings, with potential applications in other complex environments. Future work aims to develop a system for detecting complex event processes by integrating different utilities.

Comparative Bioaccesibility Study of Cereal-Based Nutraceutical Ingredients Using INFOGEST Static, Semi-Dynamic and Dynamic In Vitro Gastrointestinal Digestion.

Efficient development of effective functional foods and nutraceuticals requires adequate estimation methods of the bioaccessibility of their bioactive compounds. Specially grain-based nutraceuticals and functional ingredients are often enriched in bound/low bioavailable bioactive phytochemicals. The objective of this work was to evaluate the differences in applying static or dynamic digestion models for the estimation of bioaccessibility of antioxidants present in cereal grain-based/fiber-rich ingredients produced using enzymatic hydrolysis and sprouting processes. Main liberated phenolic compounds, antioxidant activity (ABTS•+ and ORAC) and ferric reducing capacity were evaluated in the samples following three digestion protocols with differences based on their dynamism: static, semi-dynamic and dynamic. The samples digested with the dynamic method showed higher antioxidant and reducing capacities than those digested with the static and semi-dynamic protocols. The results obtained from the digests with the dynamic model showed a total phenol content (TPs) ranging from 1068.22 to 1456.65 μmol GAE 100 g-1 and antioxidant capacity values from 7944.62 to 15,641.90 μmol TE 100 g-1 (ORAC) and from 8454.08 to 11,002.64 μmol TE 100 g-1 (ABTS•+), with a reducing power ranging from 2103.32 to 2679.78 mmol Fe reduced 100 g-1 (FRAP). The dynamic character of the protocols used for developing bioactive cereal-based foods significantly affects the estimation of their bioaccessibility, probably giving a better approach to their potential bioavailability in in vivo systems.

Machine learning based prediction for bulk porosity and static elastic modulus of Yungang Grottoes sandstone

In this work, four mainstream machine learning (ML) techniques are used to evaluate the bulk porosity and static elastic modulus of weathered Yungang Grottoes sandstone. Datasets are gathered from the experiments, which includes 432 groups effective experimental data including 8 inputs features. bulk porosity and static elastic modulus were considered as outputs to determine the weathering degrees of Yungang Grottoes sandstone. The 4 performance criteria were used to evaluate the ML models. Results demonstrate that the Artificial Neural Network (ANN) is the best-fitted models for estimating the bulk porosity and static elastic modulus compared to Multiple Linear Regression (MLR), Support Vector Regression (SVR), Gaussian Process Regression (GPR). The accuracy of the trained model for static elastic modulus is slightly higher than that of bulk porosity. The GPR and ANN model can accurately predict the bulk porosity and static elastic modulus in training stages. The ANN with multi-hidden layers developed is competent with high degree of precision and generalization ability for bulk porosity and static elastic modulus compared to other selected regression-based ML models (MLR, SVR, and GPR). The coefficient of determinations of ANN in the range of (0.9537–0.9641) during the testing stages is more stable and higher than that of (0.8883–0.9453) other built ML models. The prediction efficiency of pretrained ANN model was well adjusted for the actual and forecast datasets at the training and testing stages, and the error range was no more than 0.7% and 0.15 GPa at both stages of prediction for bulk porosity and static elastic modulus respectively. And the ANN based static elastic modulus prediction model’s error proportions significantly decreased and were confined to a modest range between + 10% and − 10%. The proposed surrogate models are valid for the bulk porosity ranging from 7 to 14% and the static elastic modulus ranging from 0.7 to 1.4 Gpa, which can be utilized for the accurate and fast prediction of the weathering degrees of Yungang Grottoes sandstone.

A study on the temperature rise characteristics of high-speed ball bearings under starvation lubrication

Purpose This paper aims to study the problem of starvation lubrication of high-speed ball bearings due to temperature rise during operation and to avoid thermal failure of bearing lubrication. Design/methodology/approach Under the quasi-statics model of grease lubrication, both the oil film dragging force and the rolling friction between the balls and raceways collectively counteract the gyroscopic torque. Initially, the static model for grease lubrication is solved, followed by calculating the generated heat using the local heat generation method and ultimately the multinodal thermal network model is solved, and the solved results of the quasi-statics are updated by the temperatures of the grease nodes based on the relationship of the grease temperature and viscosity, as well as the relationship of the viscosity and the film thickness. Findings By comparing the numerical calculation results of bearings under different working conditions, the influence of starvation lubrication on the oil film thickness, oil film drag force and rolling friction of bearings is discussed, and it is found that the numerical calculation results of the outer ring temperature of bearings under the starvation lubrication due to the consideration of temperature rise are closer to the experimental values. Originality/value This study reveals the dynamic characteristics of bearings under starvation lubrication, which is more practical and engineering guiding significance for the design of bearings, and introduces a new method and basis for the calculation of temperature rise of rolling bearings. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2024-0208/

Three‐dimensional static reservoir modelling of Kareem sandstone reservoir, Tawilla Oil Field, at the southern region of Gulf of Suez, Egypt

The Gulf of Suez, which contains Egypt's oldest oil fields, is one of North Africa's most well‐known oil regions. There are more than 80 conventional oil fields in Egypt's Gulf of Suez, some of which have reservoirs that stretch back to the Precambrian and Quaternary. In close proximity to the southern entrance of the Gulf of Suez is the Tawilla West oilfield. The oil field Tawilla West is believed to consist of rotating fault blocks that descend in a south‐west direction. The main producing reservoirs are the Miocene section reservoirs, the Belayim and Kareem sandstones. The current research is focusing on the structural elements affecting this giant field to update the field structural model using the newly processed 3D seismic survey, the acquired data from newly drilled wells and the associated different logging techniques. The seismic information quality varied from poor to fair. The quality of the interpreted stratigraphic horizons and geological faults was mainly controlled by the seismic information quality. The research used seismic attribute analyses to improve interpretation and incorporate additional features, enabling better hydrocarbon potential identification and characterization of the reservoirs. Several geological structure contour maps and cross‐sections were generated to help in delineating and understanding the reservoir's extension. Based on the detailed correlation study, we were able to detect the faults that affected the structure of the Tawilla West field in detail, define their throw amounts and directions, and identify the missed sections across the studied area. This study introduces an updated model scenario to show the differences and their effect on the field development plan and recommendations. By examining subsurface geologic structural characteristics and evaluating petrophysical data, a 3D static reservoir model was created to resolve structural settings and hydrocarbon trapping, providing detailed information on the field and identifying new opportunities for future development. The research discovered that the updated detailed 3D structural model may support the Kareem Reservoir development plans and encourage drilling, workover and dynamic operations to assign development possibilities in the correct area. According to the established model, there are at least three options in the study's attic areas that might boost oil output and oil reserves for the field while avoiding further failures.