Conventional Evaluation Methods Research Articles

Construction and evaluation of a model for efficient identification of photothermal sensitivity of tobacco cultivars based on agronomic traits.

The photothermal sensitivity of tobacco refers to the degree to which tobacco responds to changes in light and temperature conditions in its growth environment, which is crucial for determining the planting area of cultivars and improving tobacco yield and quality. In order to accurately and effectively evaluate the photothermal sensitivity of tobacco cultivars, this study selected five cultivars and their hybrid combinations with significant differences planted under different ecological conditions from 2021 to 2022 as materials. The experiment was conducted in two locations with significant differences in temperature and light. We measured the agronomic traits and biomass of the experimental materials, and constructed an effective tobacco photothermal sensitivity evaluation model using principal component analysis, membership function, and regression analysis. The reliability of the model was evaluated by utilizing the photosynthetic characteristics, chlorophyll content, and antioxidant enzyme system activity of the experimental materials. The results showed that tobacco biomass is the most important principal component in agricultural traits, and the comprehensive evaluation model for tobacco photothermal sensitivity is: y = 0.4571y1 + 0.2406y2 + 0.1725y3, where the fitting coefficients R2 of y1, y2, and y3 are 0.945, 0.851, and 0.977, respectively; The photothermal sensitivity of the experimental materials was calculated using this model, and the comprehensive ranking of the 11 experimental materials is: G3 < G5 < G10 < G9 < G11 < G6 < G7 < G2 < G4 < G8 < G1. Conventional identification methods have found that G2, G4, G6, G7, G8, and G11 are sensitive materials, G3, G5, and G10 are insensitive materials, and G1 and G9 are intermediate materials. The consistency rate of the evaluation results of the two methods reached 90.91%. And there is a significant correlation between the agronomic traits selected in the model and the physiological indicators selected by conventional evaluation methods, providing a scientific basis for evaluating the light temperature sensitivity of tobacco cultivars using agronomic traits in this study. The results indicate that the photothermal sensitivity evaluation model established in this study provides an efficient, convenient, and reliable method for evaluating the photothermal sensitivity of tobacco.

Application of Machine Learning to Predict the Capacity of Fractured Horizontal Wells in Shale Reservoirs

Shale oil wells typically have numerous volume fracturing segments in their horizontal sections, resulting in significant variability in productivity across these segments. Conventional productivity prediction and fracturing effect evaluation methods are challenging to apply effectively. Establishing a stable and efficient intelligent productivity prediction method using machine learning is a promising approach for the effective development of shale oil reservoirs. This study is based on geological data, fracturing records, and a production database of 91 production wells in a shale oil reservoir in a specific area. Fourteen key parameters affecting productivity were selected from geological and engineering perspectives, and the recursive feature elimination method based on support vector machines identified five optimal main controlling factors. Three machine learning methods—decision tree, random forest, and gradient boosting decision tree (GBDT)—were used to model productivity prediction, with root mean square error (RMSE) employed to evaluate model performance. The study results indicate that formation coefficient, cluster spacing, treatment volume, sand volume, and fracturing segment length are the main controlling factors influencing productivity in fractured horizontal wells. Among the models, the random forest algorithm with bootstrap sampling produced the most stable prediction results, achieving a prediction accuracy of 94% and an RMSE of 0.934 on the test set, outperforming the decision tree and GBDT models in terms of minimum RMSE on the test set.

Machine learning­based approaches for enhancing human resource management using automated employee performance prediction systems

Purpose This study focuses on enhancing the accuracy and efficiency of employee performance prediction to enhance decision making and improve organisational productivity. By introducing advance machine learning (ML) techniques, this study aims to create a more reliable and data-driven approach to evaluate employee performance. Design/methodology/approach In this study, nine machine learning (ML) models were used for forecasting employee performance: Random Forest, AdaBoost, CatBoost, LGB Classifier, SVM, KNN, XGBoost, Decision Tree and one Hybrid model (SVM + XGBoost). Each ML model is trained on an HR data set covering various features such as employee demographics, job-related factors and past performance records, ensuring reliable performance predictions. Feature scaling techniques, namely, min-max scaling, Standard Scaler and PCA, have been used to enhance the effectiveness of employee performance prediction. The models are trained to classify data, predicting whether an employee’s performance meets expectations or needs improvement. Findings All proposed models used in the study can correctly categorize data with an average accuracy of 94%. Notably, the Random Forest model demonstrates the highest accuracy across all three scaling techniques, achieving optimise accuracy, respectively. The results presented have significant implications for HR procedures, providing businesses with the opportunity to make data-driven decisions, improve personnel management and foster a more effective and productive workforce. Research limitations/implications The scope of the used data set limits the study, despite our models delivering high accuracy. Further research could extend to different data sets or more diverse organisational settings to validate the model’s effectiveness across various contexts. Practical implications The proposed ML models in the study provide essential tools for HR departments, enabling them to make more informed data driven decisions with regard to employee performance. This approach can enhance personnel management, improve workforce productivity and fostering a more effective organisational environment. Social implications Although AI models have shown promising outcomes, it is crucial to recognise the constraints and difficulties involved in their use. To ensure the fair and responsible use of AI in employee performance prediction, ethical considerations, privacy problems and any biases in the data should be properly addressed. Future work will be required to improve and broaden the capabilities of AI models in predicting employee performance. Originality/value This study introduces an exclusive combination of ML models for accurately predicting employee performance. By employing these advanced techniques, the study offers novel insight into how organisations might transition from a conventional evaluation method to a more advanced and objective, data-backed approach.

Deep learning based highly accurate transplanted bioengineered corneal equivalent thickness measurement using optical coherence tomography

Corneal transplantation is the primary treatment for irreversible corneal diseases, but due to limited donor availability, bioengineered corneal equivalents are being developed as a solution, with biocompatibility, structural integrity, and physical function considered key factors. Since conventional evaluation methods may not fully capture the complex properties of the cornea, there is a need for advanced imaging and assessment techniques. In this study, we proposed a deep learning-based automatic segmentation method for transplanted bioengineered corneal equivalents using optical coherence tomography to achieve a highly accurate evaluation of graft integrity and biocompatibility. Our method provides quantitative individual thickness values, detailed maps, and volume measurements of the bioengineered corneal equivalents, and has been validated through 14 days of monitoring. Based on the results, it is expected to have high clinical utility as a quantitative assessment method for human keratoplasties, including automatic opacity area segmentation and implanted graft part extraction, beyond animal studies.

Enhancing permeability estimation methods in Brazilian pre-salt complex carbonate reservoirs

Carbonate pre-salt reservoirs pose formidable challenges for formation evaluation and estimation of petrophysical properties such as rock flow capacity. The high complexity of the pore system and its textural heterogeneity often lead to poor predictions when using conventional formation evaluation methods. This paper discusses and tests the applicability of two approaches to calculate the absolute permeability curves for the carbonate reservoirs of the Barra Velha Formation in the Santos Basin, proposing a fine-tuned workflow for these complex rocks. In developing our workflow, we used data from a case study well, comprising a complete well log suite and two formation tests, and routine core analysis results (RCAL) in core plugs and sidewall cores. Our workflow consists of: (1) reinterpreting the nuclear magnetic resonance (NMR) log in vugular intervals by decomposing the transversal relaxation time spectra; (2) calculating the absolute permeability by the Timur–Coates model and adjusting the coefficients with the RCAL results from representative samples; (3) applying the Flow Zone Indicator (FZI) method for rock typing to optimally define Hydraulic Flow Units (HFU); (4) checking the consistency of HFUs regarding pore geometry and lithological characteristics; (5) evaluating the performance of the models against core data and formation tests. The predicted permeability curves performed well for the NMR and FZI models when compared to the RCAL data from representative samples, with mean absolute errors for the logarithm of permeabilities equal to 0.78 and 0.87, respectively. This represents an improvement of 15% and 5% when compared to the simplistic approach that uses a power law to relate porosity to permeability from RCAL. These curves also reasonably reproduce the results from the formation tests for the top part of the reservoir, where matrix porosity dominates the flow. However, the NMR and FZI permeability curves underestimate the test results at the bottom part of the reservoir, where karstic and fracture features dominate, demonstrating limitations in these permeability estimation methods for this specific scenario. From our observations, we propose several improvements to enhance the current methods for estimating absolute permeability in pre-salt carbonate reservoirs. In the case of the NMR approach, it is essential to consider the effects of mud invasion in vugs for the correct estimation of the proportions of free and irreducible fluids. In the FZI approach, optimization in defining the quantity and limits of hydraulic flow units is important, which shows that four units are sufficient to represent the reservoir interval.

A Decade of Computational Mass Spectrometry from Reference Spectra to Deep Learning.

Computational methods are playing an increasingly important role as a complement to conventional data evaluation methods in analytical chemistry, and particularly mass spectrometry. Computational mass spectrometry (CompMS) is the application of computational methods on mass spectrometry data. Herein, advances in CompMS for small molecule chemistry are discussed in the areas of spectral libraries, spectrum prediction, and tentative structure identification (annotation): Automatic spectrum curation is facilitating the expansion of openly available spectral libraries, a crucial resource both for compound annotation directly and as a resource for machine learning algorithms. Spectrum prediction and molecular fingerprint prediction have emerged as two key approaches to compound annotation. For both, multiple methods based on classical machine learning and deep learning have been developed. Driven by advances in deep learning-based generative chemistry, de novo structure generation from fragment spectra is emerging as a new field of research. This review highlights key publications in these fields, including our approaches RMassBank (automatic spectrum curation) and MSNovelist (de novo structure generation).

A Deep Federated Learning-Based User Credit Evaluation Model Under Financial Internet of Things Scenarios

With the rapid development of Internet of Things (IoT) technology and the digital transformation of the financial industry, the financial IoT is becoming an important trend in the future financial field. In the era of financial IoT, a large amount of data information is recorded by sensors and devices, which poses new challenges and opportunities for user credit evaluation. In this context, conventional deep learning-based user credit evaluation methods often cannot meet privacy needs. The paper combines the privacy security ability of federated learning with deep learning, and proposes a deep federated learning-based user credit evaluation model under financial IoT scenarios. First, a particle swarm optimization-based backpropagation neural network algorithm is formulated to extract user credit evaluation features, in order to obtain user behavior patterns and credit features. Then, the XGBoost algorithm is employed to output credit evaluation results. As for the training process, the thought of federated learning is integrated to distribute the model to individual participants. In such mode, the model can be updated and trained without exposing user data to the central cloud. The experiments are conducted on real-world data to assess the proposal’s performance. The results show that the proposed credit evaluation framework can achieve better accuracy, under the guarantee of user privacy.

An interpretable ensemble machine-learning workflow for permeability predictions in tight sandstone reservoirs using logging data

Tight sandstone reservoirs exhibit strong vertical heterogeneity and complex pore structures, challenging conventional permeability evaluation methods based on well-logging data. Although rising machine-learning (ML) techniques have demonstrated excellent accuracy for industrial applications, the physics and rationality within such a powerful “black box” remain less clear. Hence, reliable permeability prediction would benefit from an interpretable ML-based workflow that could reveal the controlling factors. To compare the models and examine the underlying features, 16 different ML submodels are tested after data preprocessing, feature selection, and hyperparameter optimization. By comparing the fitting accuracy and tuning time, the light gradient boosting machine optimized by the whale optimization algorithm, referred to as LGB-WOA, is determined to be the optimal model with the best fitting accuracy and relatively short tuning time. A field data application demonstrates that even in highly heterogeneous reservoir sections, the LGB-WOA model outperformed conventional petrophysical models by being the most consistent with reservoir permeability directly measured from the core samples ([Formula: see text]). The Shapley additive explanation values are then used to interpret the predictions of our LGB-WOA model. As expected, the porosity curve exhibits the highest feature importance among all input features, significantly contributing to permeability predictions. Conversely, a wellbore diameter and compensated neutron log contribute the least and need not be used for subsequent model improvements. These experiments and workflow provide a powerful method for accurately assessing the permeability in complex reservoirs and contribute to a broader understanding of the application of ML in reservoir characterization, paving the way for establishing more interpretable and reliable prediction models.

Elucidating the impact of non-spherical morphology on kinetic behavior of graphite using single-particle microelectrode

The precise assessment of the kinetic properties of graphite is crucial for the design of the material and the development of electrochemical models for lithium-ion batteries. However, conventional evaluation methods using composite electrodes fall short in excluding the influence of its porous structure and evaluating the influence of particle shape on kinetic behavior. In this work, we elucidate the impact of non-spherical morphology on kinetic behavior of single artificial graphite particle, employing the single-particle microelectrode technique. Three single artificial graphite particles with different sizes are pre-cycled and exhibit outstanding rate capability, maintaining a maximum capacity retention of 78.1 % at 200 C. Further, exchange current density and diffusion coefficients are determined through Tafel plots and the potentiostatic intermittent titration technique (PITT), respectively. It is found that the particle with a higher exposure of edge planes present elevated exchange current density and diffusion coefficients. Considering that the graphite particles typically feature an ellipsoidal rather than spherical shape, we propose an approach based on an ellipsoidal diffusion model for extracting diffusion coefficients. This model takes into account the non-spherical morphology of graphite, addressing the limitations inherent in the geometric assumptions of previous methods. The average relative error between the diffusion coefficients derived from the ellipsoidal diffusion model and the previous method using spherical assumptions is 215 %, indicating that accurately depicting the shape of ellipsoidal graphite particles in the model is crucial for obtaining correct estimates of kinetic parameters. This study offers direct experimental evidence of superior kinetic behavior of edge planes over basal planes. To leverage this characteristic, it is recommended to employ an out-of-plane aligned architecture for composite electrodes using novel techniques, e.g., 3D printing or magnetic alignment techniques.

Critical thinking, assessment, and educational policy in Palestinian universities

This study examines the relationship between critical thinking and grades at the tertiary level, focusing on their social, political, and ethical implications. Employing a mixed-methods approach, this study combines survey data collection with in-depth interviews to generate comprehensive insights into the complex relationship between critical thinking and grades. The survey targets 173 faculty members, while the interviews focus on seven selected academic staff members from Palestinian universities, enabling a comprehensive understanding of the research objectives. Results showed that grades often hinder critical thinking skills and creativity, leading to rote memorization and limited creativity. The study also highlights the political implications of grades, as standardized testing influences education policies and curriculum decisions. Faculty members expressed criticism of the prioritization of grades, citing conventional evaluation methods, temporal limitations, and resource constraints. As critical thinking is crucial for comprehensive student development, contributing to problem-solving, decision-making, creativity, innovation, effective communication, and active citizenship, the study proposes diverse approaches to strike a balance between valuing grades and nurturing critical thinking abilities. By fostering critical thinking abilities, Palestinian students can enhance their preparedness for academic pursuits, personal growth, and societal contributions.

NIRO200NX: Reliable Monitoring System for Buried Deep Inferior Epigastric Perforator Flap.

The deep inferior epigastric perforator (DIEP) flap is a useful tool for breast reconstruction and tends to be transferred into the breast envelope as the buried flap from an aesthetic point of view. However, it is difficult to monitor the blood flow in the buried DIEP flap after reconstructive microsurgery. Near-infrared spectroscopy devices have recently been used for monitoring the blood flow of various organs. NIRO200NX (Hamamatsu Photonics) continuously measures the tissue oxygen index (TOI) and quickly reflects changes in flap blood flow. In this study, we investigated whether and how much the NIRO200NX applies to monitoring the blood flow of the buried flap. We included 156 patients who underwent breast reconstruction using a DIEP flap from October 2013 to May 2022, comprising 57 exposed and 99 buried-type DIEP flap cases. We measured TOI using NIRO200NX, in combination with conventional evaluation methods, including color check, pinprick test, and Doppler sound. A criterion of TOI 50 gave the best evaluations. All the 57 exposed-type flap cases showed no false evaluations, and NIRO200NX performed precise judgment. In 99 buried-type flap cases, NIRO200NX correctly evaluated 96 cases. For those buried-type cases, we found only two false-positive and one false-negative case. The misjudgments by NIRO200NX were likely caused by hematoma. We propose NIRO200NX as a reliable device for monitoring the blood flow of the DIEP flap and predicting the outcomes of breast reconstruction by the DIEP flap transfer.

A novel methodology utilizing microchip implants to monitor individual activity and body temperature for assessing knee pain in group-housed rats

The pain assessment in animals is challenging as they cannot verbally express the site and severity of pain. In this study, we tried a small implantable actimeter, “Nanotag”, to monitor spontaneous locomotor activity and body temperature in animals suffering from a chemical-induced rat knee arthritis as compared to naïve and steroid-treated rats. Nanotag could detect the decrease in locomotor activity quickly after the arthritis induction and anti-inflammation analgesic treatment by intra-articular injection of steroid significantly improved locomotor activity. These changes were in the same line with those of a conventional knee pain evaluation method (incapacitance test). Nanotag can be utilized as the non-interventional, continuous, and completely objective monitoring the amount of pain in rat knee arthritis model. This traditional yet innovative method may be universally applicable to various pain models and species, making it a worthwhile device for research across diverse fields.

Hygrothermal Degradation of Epoxy Electrical Insulating Material-Testing and Mathematical Modeling.

The degradation of electrical insulating materials has been a subject of interest for decades as they are commonly applied in many fields of electrical engineering. Suitably modeling such a process is important since the known and well-described degradation process reveals the effect of ambient conditions, and this allows us to possibly estimate a material's remaining useful life. However, not many studies are dealing with the effect of the hygrothermal degradation of impregnating mono-component epoxy resins in the context of electrical engineering. Therefore, this study deals with this issue and discusses both the dielectric response (based on the measurement of relative permittivity, dissipation factor, and dielectric strength) and the mechanical response (based on measurements of tensile strength and Shore D hardness) to a hygrothermal degradation experiment. In addition, the results of thermal analyses are presented for the evaluation of the pristine specimen manufacturing process and possible post-curing processes. Furthermore, this study presents several methodologies for modeling the degradation process, including a novel methodology in this area based on Bayesian experimental design. As an outcome, mechanical parameters are proven to be specific in terms of the actual condition of the material and the Bayesian enhanced degradation model seems to be superior to the conventional evaluation methods in this particular study.

A dual-labeling molecule for efficient drug discovery of mitochondrial-lysosomal interactions

The close association between organelle interactions, such as mitochondrial–lysosomal interactions, and various diseases, including tumors, remains a challenge for drug discovering and identification. Conventional evaluation methods are often complex and multistep labeling procedures often generate false positives, such as cell damage. To overcome these limitations, we employed a single dual-color reporting molecule called Coupa, which labels mitochondria and lysosomes as blue and red, respectively. This facilitates the evaluation and discovering of drugs targeting mitochondria–lysosome contact (MLC). Using Coupa, we validated the effectiveness of various known antitumor drugs in intervening MLC by assessing their effect on key aspects, such as status, localization, and quantity. This provides evidence for the accuracy and applicability of our dual-color reporting molecule. Notably, we observed that several structural isomers of drugs, including Urolithin (A/B/C), exhibited distinct effects on MLC. In addition, Verteporfin and TEAD were found to induce anti-tumor effects by controlling MLC at the organelle level, suggesting a potential new mechanism of action. Collectively, Coupa offers a novel scientific tool for discovering drugs that target mitochondrial–lysosomal interactions. It not only distinguished the differential effects of structurally similar drugs on the same target, but also reveals new mechanisms underlying the reported antitumor properties of existing drugs. Ultimately, our findings contribute to the advancement of drug discovery and provide valuable insights into the complex interactions between organelles in a disease context.

Hydraulic loss analysis of turbodrill blade cascades based on entropy production theory

Turbodrill is a kind of high-temperature resistant downhole motor which is commonly used in drilling deep and high-temperature wells. It belongs to multi-stage axial flow turbomachinery. There are complex flow losses in turbodrill blade cascades, but conventional flow-loss evaluation methods cannot obtain the location of losses in cascades. The entropy production method constructs the relationship between flow field parameters and flow losses, so the location where flow losses generate can be obtained, which is commonly used to analyze the flow losses in various flows. In this paper, entropy production method is used to obtain the magnitude and position of hydraulic losses in turbodrill blade cascades. The single passage calculation model of three-stage stator and rotor is established, and CFD method is employed to compute the flow field parameters at different viscosities and rotary speeds. Different kinds of entropy production rates and values are calculated. Finally, the total flow loss calculated by entropy production method is compared with that calculated by pressure difference method, and the contrastive result is very similar, which indicates the correctness of entropy production method. The results show that the high entropy production regions are mainly located near the walls and the position changes with variations in rotary speed and viscosity, and the stator entropy production value is always lower than that in rotor. In addition, when the fluid viscosity is low, the main types of entropy production are turbulent entropy production and wall friction entropy production, while when the fluid viscosity is high, the main types of entropy production are direct entropy production and turbulent entropy production. Furthermore, the rotary speed variation has a greater impact on turbulent entropy production and wall friction entropy production compared to viscosity change, while the viscosity change has a greater impact on direct entropy production than the variation rotary speed. This paper introduces entropy production method into flow losses analysis for turbodrill, presenting a new approach for studying flow losses and providing a reference for improving turbodrill design.

Fluorescence Confocal Microscopy Can Accelerate Diagnosis of Cervical Lymphadenopathy

Fluorescence confocal microscopy (FCM) is an optical technique that uses laser light sources of different wavelengths to generate real-time images of fresh, unfixed tissue specimens. Unlike conventional histologic evaluation methods, FCM is able to assess fresh tissue samples without the associated cryo artifacts typically observed after frozen sectioning. The purpose of this study was to evaluate the utility of FCM imaging in the differential diagnosis of cervical lymphadenopathy. Twenty-two cervical lymph node specimens from patients with lymphadenopathy of unknown origin were imaged by FCM. Two pathologists independently evaluated the scans for suspicion of malignancy and preliminary diagnosis. Malignancy was reliably excluded or confirmed by both pathologists with a sensitivity of 90.9% for pathologist 1 and 100% for pathologist 2. The specificity was 100% for both pathologists. For the preliminary diagnosis, almost perfect agreement with the final diagnosis was observed for both pathologists (κ = 0.94 for pathologist 1 and κ = 1.00 for pathologist 2). This is the first study to investigate lymph node specimens with different diagnoses, including lymphoma, using FCM. Our results indicate that differential diagnosis of lymph node specimens is feasible in FCM images, thus encouraging further exploration of FCM imaging in lymph node specimens to accelerate diagnosis and open the possibility of digitizing diagnosis on fresh, unfixed tissue.

Vocational Education Skill Assessment and Intelligent Assistance: A Study on the Application of Machine Learning Algorithms in the Assessment of Vocational Information Literacy Teaching Ability

Investigating vocational educators' knowledge-based teaching skills across China's Vocational Education (VE) institutions, this research focuses on the practical use of Machine Learning (ML) algorithms. Instructors' efficacy must be evaluated, and this work addresses the gap. VE performs an essential role in connecting learning abilities with the demands of industry. The investigation plans on developing an adaptable, subjective assessment technique that extends within the boundaries of conventional subjective evaluation methods using modern ML techniques such as Support Vector Machines (SVM), Decision Trees (DT), and Neural Networks (NN). Each ML model's accuracy, reliability, and feasibility have been determined using data collected from 120 vocational educators encompassing various fields and regions. Researchers predict that our findings will provide perspective on how to improve vocational education settings' teaching methods and governance.

Comprehensive three-dimensional cone beam computed tomography assessment unilateral alveolar cleft reconstruction using autologous iliac cancellous bone combined with deproteinized bovine bone: A clinical retrospective evaluation

Large-volume autologous iliac cancellous bone grafting for alveolar cleft may lead to undesirable bone resorption and susceptible donor-site morbidity, whereas the addition of deproteinized bovine bone (DBB) could optimize outcomes. This study aimed to evaluate the effectiveness of combining autologous iliac bone with DBB using three-dimensional cone beam computed tomography (3D-CBCT) for better analysis of bone generation than conventional evaluation methods. Thirty-six patients with unilateral alveolar cleft were assigned into two groups. Group A (n = 21) underwent autogenous cancellous bone graft harvested from the anterior iliac crests, while Group B (n = 15) received a composite of autogenous iliac cancellous bone and DBB. Patients in Group B displayed higher bone filling rates (P < 0.0001) and lower bone absorption rates (P < 0.001) than those in Group A at both 6 months and 1 year postoperatively. Additionally, there were directional differences in bone absorption within the bone grafts, with more absorption observed on the alveolar crest and palatal sides than that on the nasal and labial sides (P < 0.001). This study demonstrates that employing a combination of DBB and autologous bone in alveolar cleft repair achieves better outcomes of bone grafting.

An online evaluation model for mechanical/thermal states in prismatic lithium-ion batteries under fast charging/discharging

Conventional online evaluation methods for the mechanical/thermal behaviour of lithium-ion batteries fall short in terms of efficiency and accuracy, especially under extreme conditions coupling high-temperature and fast charging/discharging. This paper proposes an electrochemical/thermal/mechanical analytical model for prismatic lithium-ion batteries to assess their temperature, stress, deformation, and gas evolution. An electrochemical submodel is formulated, covering the lithium intercalation/deintercalation, solid-electrolyte interphase (SEI) decomposition/regeneration, and electrolyte decomposition. A thermal submodel is created to simulate heat transfer between the cell and its environment, considering reaction and Joule heating as heat sources. A mechanical submodel is developed to reveal the effects of nonlinear elastic constitutive properties and the mechanical/thermal/electrical states on cell deformation and stress, incorporating the reaction gas evolution. A coupled multidisciplinary analytical model is formed, using the state variables of temperature, stress, and deformation to link the submodels. The model's performance was validated against numerical and experimental results under high-temperature charge/discharge cycle conditions, demonstrating efficiency at the level of seconds, temperature errors below 0.6 %, and pressure errors below 4.6 %. The advantages in efficiency, accuracy, and applicability highlight its excellent prospects in energy storage and electric vehicle applications.

Applicability of the formability evaluation method for advanced high strength steels

The use of high strength and ultra-high strength steels has become the main technical solution for reducing vehicle weight and improving safety. It is more complicated to evaluate the formability of advanced high strength steels with different microstructure and deformation characteristics. In this paper, the applicability of existing formability evaluation methods for advanced high strength steels has been verified by experiment and theoretical analysis. The experiment and data analysis of conventional formability evaluation methods, such as strain hardening index, forming limit curve and hole expansion ratio, were carried out on advanced high strength steels, such as dual-phase steel, complex-phase steel, quenched and tempered steel and dual-phase steel with high formability, which are widely used in automotive industry. It is found that due to the complex work hardening characteristics of advanced high strength steels, the work hardening homogenization and forming limit cannot be characterized by a single work hardening index or forming limit diagram, and the standard hole expansion method cannot reflect the quality sensitivity of the formed edge. A new formability evaluation index is proposed and discussed, which can be used to more accurately compare the formability of advanced high strength steels and provide a reference for material evaluation and part selection.