Evaluation Method Research Articles

Evaluation method of Driver’s olfactory preferences: a machine learning model based on multimodal physiological signals

IntroductionAssessing the olfactory preferences of drivers can help improve the odor environment and enhance comfort during driving. However, the current evaluation methods have limited availability, including subjective evaluation, electroencephalogram, and behavioral action methods. Therefore, this study explores the potential of autonomic response signals for assessing the olfactory preferences.MethodsThis paper develops a machine learning model that classifies the olfactory preferences of drivers based on physiological signals. The dataset used for training in this study comprises 132 olfactory preference samples collected from 33 drivers in real driving environments. The dataset includes features related to heart rate variability, electrodermal activity, and respiratory signals which are baseline processed to eliminate the effects of environmental and individual differences. Six types of machine learning models (Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, K-Nearest Neighbors, and Naive Bayes) are trained and evaluated on this dataset.ResultsThe results demonstrate that all models can effectively classify driver olfactory preferences, and the decision tree model achieves the highest classification accuracy (88%) and F1-score (0.87). Additionally, compared with the dataset without baseline processing, the model’s accuracy increases by 3.50%, and the F1-score increases by 6.33% on the dataset after baseline processing.ConclusionsThe combination of physiological signals and machine learning models can effectively classify drivers' olfactory preferences. Results of this study can provide a comprehensive understanding on the olfactory preferences of drivers, ultimately enhancing driving comfort.

Deep learning-based cytoskeleton segmentation for accurate high-throughput measurement of cytoskeleton density.

Microscopic analyses of cytoskeleton organization are crucial for understanding various cellular activities, including cell proliferation and environmental responses in plants. Traditionally, assessments of cytoskeleton dynamics have been qualitative, relying on microscopy-assisted visual inspection. However, the transition to quantitative digital microscopy has introduced new technical challenges, with segmentation of cytoskeleton structures proving particularly demanding. In this study, we examined the utility of a deep learning-based segmentation method for accurate quantitative evaluation of cytoskeleton organization using confocal microscopic images of the cortical microtubules in tobacco BY-2 cells. The results showed that, although conventional methods sufficed for measurement of cytoskeleton angles and parallelness, the deep learning-based method significantly improved the accuracy of density measurements. To assess the versatility of the method, we extended our analysis to physiologically significant models in the context of changes in cytoskeleton density, namely Arabidopsis thaliana guard cells and zygotes. The deep learning-based method successfully improved the accuracy of cytoskeleton density measurements for quantitative evaluations of physiological changes in both stomatal movement in guard cells and intracellular polarization in elongating zygotes, confirming its utility in these applications. The results demonstrate the effectiveness of deep learning-based segmentation in providing precise and high-throughput measurements of cytoskeleton density, and has the potential to automate and expedite analyses of large-scale image datasets.

Sensitivity Analysis Study of Engine Control Parameters on Sustainable Engine Performance

With the increasing global concern for environmental protection and sustainable resource utilization, sustainable engine performance has become the focus of research. This study conducts a sensitivity analysis of the key parameters affecting the performance of sustainable engines, aiming to provide a scientific basis for the optimal design and operation of engines to promote the sustainable development of the transportation industry. The performance of an engine is essentially determined by the combustion process, which in turn depends on the fuel characteristics and the work cycle mode suitability of the technical architecture of the engine itself (oil-engine synergy). Currently, there is a lack of theoretical support and means of reference for the sensitivity analysis of the core parameters of oil–engine synergy. Recognizing the problems of unclear methods of defining sensitivity parameters, unclear influence mechanisms, and imperfect model construction, this paper proposes an evaluation method system composed of oil–engine synergistic sensitivity factor determination and quantitative analysis of contribution. The system contains characteristic data acquisition, model construction and research, and sensitivity analysis and application. In this paper, a hierarchical SVM regression model is constructed, with fuel physicochemical characteristics and engine control parameters as input variables, combustion process parameters as an intermediate layer, and diesel engine performance as output parameters. After substituting the characteristic data into the model, the following results were obtained, R2 > 0.9, MSE < 0.014, MAPE < 3.5%, indicating the model has high accuracy. On this basis, a sensitivity analysis was performed using the Sobol sensitivity analysis algorithm. It was concluded that the load parameters had the highest influence on the ID (ignition delay time), combustion duration (CD), and combustion temperature parameters of the combustion elements, reaching 0.24 and above. The influence weight of the main spray strategy was greater than that of the pre-injection strategy. For the sensitivity analysis of the premix ratio, the injection timing, EGR (exhaust gas recirculation) rate, and load have significant influence weights on the premix ratio, while the influence weights of the other parameters are not more than 0.10. In addition, the combustion temperature among the combustion elements has the highest influence weights on the NOx, PM (particulate matter) concentration, and mass, as well as on the BTE (brake thermal efficiency) and BSFC (brake specific fuel consumption). The ID has the highest influence weight on HC and CO at 0.35. Analysis of the influence weights of the index parameters shows that the influence weights of the fuel physicochemical parameters are much lower than those of the engine control parameters, and the influence weights of the fuel CN (cetane number) are about 5% greater than those of the volatility, which is about 3%. From the analysis of the proportion of index parameters, the engine control parameter influence weights are in the following order: load > EGR > injection timing > injection pressure > pre-injection timing> pre-injection ratio.

Semi-quantitative scoring criteria based on multiple staining methods combined with machine learning to evaluate residual nuclei in decellularized matrix

Abstract The detection of residual nuclei in decellularized extracellular matrix (dECM) biomaterials is critical for ensuring their quality and biocompatibility. However, current evaluation methods have limitations in addressing impurity interference and providing intelligent analysis. In this study, we utilized four staining techniques—HE staining, acetocarmine staining, the Feulgen reaction, and DAPI staining—to detect residual nuclei in dECM biomaterials. Each staining method was quantitatively evaluated across multiple parameters, including area, perimeter, and grayscale values, to establish a semi-quantitative scoring system for residual nuclei. These quantitative data were further employed as learning indicators in machine learning models designed to automatically identify residual nuclei. The experimental results demonstrated that no single staining method alone could accurately differentiate between nuclei and impurities. In this study, a semi-quantitative scoring table was developed. With this table, the accuracy of determining whether a single suspicious point is a cell nucleus has reached over 98%. By combining four staining methods, false positives caused by impurity contamination were eliminated. The automatic recognition model trained based on nuclear parameter features reached the optimal index of the model after several iterations of training in 172 epochs. The trained artificial intelligence model achieved a recognition accuracy of over 90% for detecting residual nuclei. The use of multi-dimensional parameters, integrated with machine learning, significantly improved the accuracy of identifying nuclear residues in dECM slices. This approach provides a more reliable and objective method for evaluating dECM biomaterials, while also increasing detection efficiency.

The Influence of Stability in New Power Systems with the Addition of Phase Modulation Functions in Thermal Power Units

The addition of phase modulation function technology to thermal power units is one of the most effective measures to solve dynamic reactive power shortages in the construction process of new power systems. In this paper, the influence of the phase modulation function transformation of thermal power units on the stability of a new power system is studied. Firstly, the new power system stability index is deeply analyzed, and an evaluation system for power system transient stability is constructed from five key dimensions: transient voltage, static voltage, power angle stability, power flow characteristics, and grid support. Secondly, a fuzzy comprehensive evaluation method considering the subjective and objective comprehensive weights is proposed, and the influence of the phase modulation transformation of the thermal power unit on the stability of the receiving-end power grid is quantitatively analyzed. Finally, a CEPRI36 node example model was built based on the PSASP v.7.91.04.9258 (China Electric Power Research Institute, Beijing, China) platform to verify the accuracy and effectiveness of the proposed method. The results show that the proposed method can quantitatively analyze the impact of adding a phase modulation function to thermal power units on the stability of the power system. At the point of renewable energy connection, the static voltage stability index improved by 42.9%, the transient power angle stability index improved by 32.1%, the multi-feed effective short-circuit ratio index improved by 33.9%, and the comprehensive evaluation score improved by 14.7%. These results further indicate that adding a phase modulation function to thermal power units can provide a large amount of dynamic reactive power support and improve the voltage stability and operational flexibility of the system.

Genetic Profiles of Purine, Uric Acid, Superoxide Dismutase, and Growth in Thai Slow-Growing Chickens

The objective of this study was to estimate genetic parameters and genetic correlations between growth characteristics and purine and uric acid in the breast and liver and superoxide dismutase (SOD) in the blood. The growth characteristics included body weight (BW) at hatching (BW0), BW at 2, 4, 6, 8, and 10 weeks of age, average daily gain (ADG) at 0–2, 2–4, 4–6, 6–8, and 8–10 weeks of age, and breast circumference at 6, 8, and 10 weeks of age (BrC6, BrC8, and BrC10) were recorded from 300 Thai native chickens (Shee breed). In total, 30 chickens (15 males and 15 females) were randomly euthanized to collect breast meat, liver, and blood samples to determine the purine content. A multiple-trait animal model and an average information-restricted maximum likelihood (AI-REML) were used to estimate the variance components and genetic parameters. The estimated heritability values for all growth traits were moderate and ranged from 0.304 to 0.485, 0.270 to 0.335, and 0.286 to 0.314 for BW, ADG, and BrC, respectively. The estimated heritability values for various biochemical traits, including purine content, uric acid, and SOD levels, were low to moderate and ranged from 0.035 to 0.143, and 0.050 to 0.213 in breast meat and liver, respectively. In genetic correlations, total purine content showed a strong negative correlation with growth traits, whereas uric acid and SOD levels exhibited varying degrees of correlation with BW and ADG. These results highlight the importance of genetic parameters between growth and biochemical traits in Thai native chickens and provide valuable insights for breeding programs aimed at improving growth performance and meat quality. This study indicated the potential use of heritability values and genetic correlations to enhance selective breeding strategies using the multiple-trait genetic evaluation method for optimal trait combinations in poultry.

Quality Markers of Fufang Shenqi Oral Liquid Based on Integrated Fingerprint Analysis, Chemical Pattern Recognition, and Quantification.

Fufang Shenqi Oral Liquid (FFSQOL) is an important Chinese medicine compound preparation with a wide range of clinical applications, which is mainly used to regulate immune function, improve cardiovascular function, and have anti-inflammatory and antibacterial effects. At present, it is of great importance to establish the quality evaluation method of FFSQOL and to investigate its quality markers (Q-markers). The aim of this study is to establish a quality evaluation method for FFSQOL and screen its Q-markers to provide a scientific basis for its quality control. Fourteen batches of FFSQOL were subjected to high-performance liquid chromatography (HPLC) fingerprint and similarity analysis. The components of FFSQOL were identified, and their content was determined. This was combined with cluster analysis (CA) and principal component analysis (PCA) to determine the Q-markers of FFSQOL. In this study, an HPLC fingerprint was established for 14 batches of FFSQOL, identifying 12 common peaks and six major components. Four components were identified as stable and reproducible: gallic acid (504.94 ~ 1219.04 μg/mL), caffeic acid (452.15 ~ 783.01 μg/mL), 7-O-glucoside (1097.72 ~ 2440.41 μg/mL), and formononetin (176.2 ~ 177.51 μg/mL). Quality evaluation of the 14 batches was conducted using chemical pattern recognition analysis. CA results indicated two distinct groups, and PCA revealed that principal components 1 and 2 were the main factors influencing batch differences. A combination of HPLC fingerprint, content determination results, and chemical pattern recognition analysis was employed to identify Q-markers for FFSQOL. The markers identified were gallic acid, caffeic acid, calycosin 7-O-glucoside, and formononetin. In this study, a quality evaluation method for FFSQOL was established through the implementation of fingerprint, content determination, and chemical pattern recognition analysis, resulting in the identification of four Q-Markers of FFSQOL, which laid the foundation for the formulation of FFSQOL quality standards.

Achieving inter- and transdisciplinarity in Ecohealth: insights from a rodent-borne disease project in a polycrisis era

IntroductionInter- and transdisciplinary research (ITDR) is increasingly promoted to address “wicked problems”, particularly in health sectors adopting approaches like Ecohealth. Our Ecohealth-inspired project on rodent-borne diseases, initiated just before the COVID-19 pandemic, provided an opportunity to evaluate ITDR implementation.MethodsWe employed a recently developed semi-quantitative evaluation method to measure our project’s success in achieving ITDR and analyzed factors influencing this achievement.ResultsThe project showed strengths in system description, team task allocation, and data sharing, but had lower scores in engaging societal actors throughout the project cycle.DiscussionWe identified the underexplored influence of problem wickedness as a critical determinant of ITDR success. Addressing rodent-borne diseases, a less wicked problem, limited engagement potential but enabled constructive dialog with local actors. These insights are vital for addressing variably wicked problems in a polycrisis era. We propose recommendations to strengthen researchers’ capacities, particularly in Ecohealth.

Squirrel search algorithm-support vector machine: Assessing civil engineering budgeting course using an SSA-optimized SVM model

Abstract In the field of civil engineering education, accurately evaluating the effectiveness of budget courses is crucial. However, traditional methods of evaluation tend to be cumbersome and subjective. In recent years, machine learning technology has demonstrated immense potential in educational evaluation. Nevertheless, in practical application, the machine learning-based evaluation model for civil engineering budget courses faces the predicament of inadequate evaluation accuracy. To solve this problem, the squirrel search algorithm technology was used to establish support vector machine parameters and create optimization algorithms. The performance of the proposed optimization algorithm was tested, and the results showed that the accuracy of the proposed algorithm was 0.927, which was better than similar prediction algorithms. Then, the empirical analysis of the proposed civil engineering budget course evaluation model showed that student satisfaction and student examination scores had increased to 92 and 94 points, respectively. The above results reveal that the proposed optimization algorithm and course evaluation model have good performance. Therefore, the implementation of the proposed curriculum evaluation method can significantly improve the learning efficiency of students and the teaching quality of civil engineering budgeting methods courses.

Standardized evaluation of diabetic retinopathy using artificial intelligence and its association with metabolic dysfunction-associated steatotic liver disease in Japan: A cross-sectional study.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is common in patients with obesity and diabetes and can lead to serious complications. This study aimed to evaluate fundus photographs using artificial intelligence to explore the relationships between diabetic retinopathy (DR), MASLD, and related factors. In this cross-sectional study, we included 1,736 patients with a history of diabetes treatment or glycated hemoglobin (HbA1c) levels of ≥6.5%. All participants were negative for hepatitis B surface antigen and hepatitis C virus antibody and were selected from 33,022 examinees at a health facility in Japan. Fundus photographs were analyzed using RetCAD software, and DR scores were quantified. The presence of DR was determined using two cutoffs: sensitivity (CO20) and specificity (CO50). Steatotic liver (SL) stages were assessed via ultrasound and fibrosis indices and classified into three groups: no SL (SL0), SL with low fibrosis (SL1), and SL with high fibrosis (SL2). Odds ratios (ORs) for the risk of DR associated with each SL stage were calculated using logistic regression, adjusted for age, sex, body mass index, HbA1c, C-reactive protein level, and alcohol consumption. The risk of DR was lower in the SL1 (OR: 0.63, 0.54) and SL2 (OR: 0.64, 0.77) groups compared to the SL0 group at CO20 for both the Fibrosis-4 Index (FIB-4) and the non-alcoholic fatty liver disease fibrosis score (NFS), respectively. Additionally, higher levels of cholinesterase were consistently associated with a reduced risk of DR (FIB-4: OR 0.52, NFS: OR 0.54) at CO50. This study demonstrates that MASLD was associated with a reduced risk of DR, with cholinesterase levels providing further protective effects, highlighting the need for further research into the protective mechanisms and refinement of DR evaluation techniques. The standardized AI evaluation method for DR offers a reliable approach for analyzing retinal changes.

Applying Predictive Safety Analysis to Complete Streets: A Case Study

Complete Streets encompasses policymaking, planning, design, and operations to improve safety, connectivity, and equity for all road users. Existing safety performance analysis and evaluation methods have gaps related to predicting the safety benefits of Complete Streets projects. Specifically, the methods do not fully capture expected multi-modal safety performance effects or address the combined effects of multiple safety treatments implemented in combination. This paper describes a research effort to 1) apply predictive safety analysis methods to quantify the safety performance effects of Complete Streets projects in the project planning stage and 2) compare the predicted safety performance effects with actual effects estimated with a project evaluation after construction. The paper serves as a basis for understanding the current capabilities, challenges, and needs associated with safety performance analysis of Complete Streets projects.

SERIAL HEALTH ASSESSMENT WITH STANDARDIZED EXTERNAL SCORING AND HEMOLYMPH EVALUATION IN THE AMERICAN HORSESHOE CRAB (LIMULUS POLYPHEMUS).

Serial health assessment of a managed population of American horseshoe crabs (HSC; Limulus polyphemus) was performed. Twenty HSC were examined once a month for 7 mon; this included a physical exam with a standardized scoring sheet, HR, morphometrics, and hemolymph evaluation with hemocyte count, copper concentration, and biochemistry panel. The HSC in this population had varying levels of external lesions at enrollment. All HSC were clinically and behaviorally stable over the study period, with no apparent differences in evaluated parameters in relation to degree of external lesions. Although the standardized scoring sheet was found to be helpful to complete the evaluation, the qualitative criteria potentially masks clinically important gradations in some parameters. HR ranged from 4 to 60 bpm, with high intra- and interindividual variability. Increased prosomal widths (PW) were found to be correlated with lower HR. Plots of weight (W) in grams divided by PW in centimeters are roughly linear, and W:PW shows promise as a method of body condition evaluation in a hard-shelled animal. Serial collection of ∼2 ml of hemolymph led to no appreciable clinical decline in any animal. Total protein levels were stable and higher than previously reported for HSC in managed care. Glucose values were stable throughout the study period, which is interpreted as reflecting adequate nutritional intake in the study animals. Further studies are needed to determine the clinical utility of standardized vertebrate biochemistry panels in invertebrate species.

THERAPEUTIC REFRACTIVE VITRECTOMY (TRV) FOR THE MANAGEMENT OF VITREOUS FLOATERS AND OPACITIES (VFO) ASSESSED BY THE STANDARDIZED AND KINETIC ANATOMICAL AND FUNCTIONAL TESTING OF VITREOUS FLOATERS AND OPACITIES (SK VFO TEST).

Propose new terminology and evaluate the effectiveness of Therapeutic Refractive Vitrectomy (TRV) for selective removal of vitreous floaters and opacities (VFO) utilizing Standardized Kinetic Anatomical Functional Testing of VFO (SK VFO Test) and new ultra widefield (UWF) OCT imaging techniques. Retrospective analysis. Twenty eyes underwent TRV for symptomatic VFO. Pre-/post-TRV assessments: SK-VFO Test, including straylight measurements (HD Analyzer, Light Disturbance Analyzer, C-Quant) alongside non-contact imaging including a new UWF OCT to evaluate changes in vitreous anatomical and optical properties. Post-TRV evaluations indicated objective changes in vitreous optical properties and subjective measures. Reduced straylight measurements: HDA 22%, LDA 54.4% (p=0.013), and C-Quant 7.8% (p=0.034). Patient reported outcomes statistically significantly improved (p=0.022). Corrected distance visual acuity (CDVA) changes were marginal. New UWF 26 mm with a 12 mm imaging window OCT facilitated detailed vitreous imaging, confirming status of posterior vitreous detachment (PVD) (100%). Post-TRV UWF and OCT imaging demonstrated restoration of vitreous clarity, confirmed presence of residual cortical vitreous and absence of new PVDs. TRV is a significant contribution for therapeutic refractive surgery as a safe and effective approach to enhancing visual quality, correcting refractive and opaque vitreous anomalies. Improvement in CDVA was minimal representing limited effectiveness as a measure of comprehensive visual function. Improvements in objective straylight measures and imaging are in alignment with subjective symptom improvements post-TRV. This underscores the utility and value of new holistic evaluation methods beyond traditional metrics to assess the impact of TRV on visual function and quality of life.

Automatic evaluation of planners in dynamic scenes during human–robot interaction

AbstractThe evaluation of planners is essential to guarantee the efficiency and performance of trajectories executed by collaborative robots in human–robot interaction (HRI) contexts. The main objective of this research is to develop a method that allows obtaining an automated assessment of the performance of planners widely used in robotic environments. In addition to analyzing specific metrics such as planning time, path length, smoothness, and clearance, this paper introduces novel contributions, including a unique method for dynamic scene evaluation and a new comprehensive and global performance index, named Total Performance Index (TPIx), which encompasses all the aforementioned metrics. The system proposed is composed of a collaborative robot and an RGB-D sensor to monitor the environment using 3D data to perform safe trajectories during multiple tasks. In this quantitative evaluation, two single-query planners (RRT, KPIECE) and two multiple-query planners (SPARS, PRM) were analyzed. The results obtained demonstrate the feasibility and effectiveness of the proposed method to automatically evaluate the planners used by collaborative robots. Therefore, this study contributes to the advancement of collaborative robotics by introducing updated methods for planner evaluation and aims to facilitate the development of more efficient and adaptive systems in light of recent advancements in robotic technologies.

Adaptive Ramanujan package decomposition method: a novel precise health indicator of rolling bearings

ABSTRACT In this paper, an adaptive Ramanujan packet decomposition (ARPD) method based on precise envelope segmentation is proposed to improve the accuracy of weak fault feature extraction and realise early non-destructive testing and condition evaluation of rolling bearings. Firstly, the power spectral density of the original signal is envelope-processed by accurate envelope segmentation method, so as to reduce the extreme points produced by interference frequency, and then realise the optimal frequency band division and reduce the influence of noise components. Secondly, the Ramanujan spectrum signal-to-noise ratio (RSSNR) index is introduced to quantify the fault characteristic information of the corresponding component in each frequency band. The largest component of the index is selected as the optimal mode, and the current number of layers is defined as the optimal number of modes. Finally, through the idea of iterative decomposition and RSSNR index, the problem that the optimal mode number is difficult to determine accurately is eliminated, and the optimal mode number is determined adaptively. The results of simulation and experimental signal analysis show that ARPD method can accurately divide the frequency bands and has excellent weak fault feature extraction ability, so it is an effective early non-destructive testing and condition evaluation method for rolling bearings.

A Review of Herbal Toothpaste Formulation, and Efficacy in Oral Care

The increasing consumer shift toward natural, plant-based, and eco-friendly products has significantly contributed to the rise in popularity of herbal toothpastes in the global oral care market. Herbal toothpastes, formulated using plant-based extracts, offer a safe and sustainable alternative to traditional synthetic toothpastes, which often contain chemicals such as fluoride, sodium lauryl sulfate, and artificial preservatives. This review paper aims to explore the key ingredients, formulation processes, and evaluation methods used in the development of herbal toothpaste, with a particular focus on their therapeutic properties, efficacy, and safety. Herbal toothpastes are typically composed of herbal extracts known for their medicinal benefits, such as neem, clove, aloe vera, tea tree oil, and peppermint, which provide antibacterial, anti-inflammatory, and antioxidant properties. Additionally, natural abrasives like calcium carbonate and silica are included for plaque removal, while natural binders and thickeners ensure proper consistency. The formulation process involves the careful selection of these ingredients, their extraction, and the preparation of a stable, homogeneous paste. Evaluation of herbal toothpaste is essential to ensure its safety, efficacy, and consumer acceptability. Microbial testing is conducted to determine the toothpaste’s ability to inhibit oral pathogens, while sensory evaluation assesses taste, texture, and aroma. Clinical studies are performed to evaluate the product's impact on gum health, plaque reduction, and remineralization of enamel. Moreover, parameters such as abrasivity and pH levels are rigorously tested to ensure that the toothpaste does not damage tooth enamel or irritate gums.

A new index for evaluating the antioxidant capacity of hemodialysis membranes using Fenton’s reagent

BackgroundHemodialysis and hemodiafiltration therapy involve supplying large amounts of blood and dialysis fluid to a hemodialyzer. Although the biocompatibility of dialysis membranes has improved recently, a standard evaluation method has not been established. In this study, we aimed to establish a standard method for evaluating the antioxidant capacity of hemodialysis membranes.MethodsWe used Fenton’s reagent to evaluate the antioxidant capacity of the spent washing solution for dialyzers and hemodialysis membranes. The dialysis membrane materials used for the test were conventional or vitamin E-coated polysulfone.ResultsThe luminescence in the first 200 mL of the spent washing solutions was significantly lower than that of the control solution, for both the polysulfone and vitamin E-coated polysulfone membranes, because the dialyzer filling solution contained sodium metabisulfite as an antioxidant. Compared with the conventional polysulfone membrane, the vitamin E-coated membrane maintained its antioxidant capacity index after exposure to Fenton’s reagent. Vitamin E-coated membranes inhibited oxidation by Fenton’s reagent 5.5% more than polysulfone membranes.ConclusionsExposure to Fenton’s reagent can serve as a standard in vitro index to evaluate the antioxidant capacity of dialysis membrane materials that require large quantities of solution.

Study on brittleness characteristics of deep shale: A case study of Lu211 well in the Luzhou block.

Ensuring the sustainability of energy is pivotal for achieving a harmonious balance between environmental conservation and economic growth. The mechanical behavior of deep shale reservoir rocks is intricate, presenting challenges in ascertaining their brittleness characteristics. To address this, the study employed a suite of evaluation techniques, encompassing analyses of stress-strain curve attributes, energy dissipation patterns, and mineral composition profiles. The overarching goal was to delineate the variations in deep shale brittleness as a function of depth. The findings indicate a general trend of decreasing shale brittleness with increasing depth. However, the brittleness indices derived from the three distinct evaluation methods varied, with the mineral composition approach yielding the most scattered results. This disparity underscores the heterogeneity of deep shale, likely due to its varied diagenetic history compared to shallower formations. In response to these observations, the study leveraged the principle of weighted averaging to devise a composite brittleness evaluation method. This innovative approach not only integrates the effects of multiple influencing factors but also accounts for the differential impact and weight of each method on the overall brittleness assessment. By doing so, it offers a more nuanced and holistic understanding of shale brittleness. The paper's exploration of deep shale's brittleness characteristics contributes valuable insights for the exploration and development of deep shale reservoirs, enhancing the strategic and operational frameworks within the energy sector. This comprehensive evaluation method serves as a foundation for more informed decision-making, ensuring that energy extraction is conducted in a manner that is both economically viable and environmentally responsible.

A binary-tree subdivision method for evaluation of singular integrals with discontinuous kernel in 3D BEM

PurposeA binary-tree subdivision method (BTSM) for numerical evaluation of weakly singular integrals with discontinuous kernel in the three-dimensional (3D) boundary element method (BEM) is presented in this paper.Design/methodology/approachIn this method, the singular boundary element is split into two sub-elements and subdivided recursively until the termination criterion is met and the subdivision is stopped. Then, the source point is surrounded by one or more spherical cavities determined by the discontinuous kernel function. The sub-elements located in spherical cavities will be eliminated, and the regular triangular or rectangle elements are employed to fill the spherical cavities.FindingsWith the proposed method, the obtained sub-elements are automatically refined as they approach the source point, and they are “good” in shape and size for standard Gaussian quadrature. Thus, the proposed method can be used to evaluate singular integrals owing discontinuous kernel function accurately for cases of different element shapes and various source point locations.Originality/valueNumerical examples show that the BTSM is suitable for planar and curved elements of arbitrary regular or irregular shape at various source point locations, and the results have much better accuracy and robustness than conventional subdivision method (CSM) when the kernel function is discontinuous.

Artificial intelligence terminology, methodology, and critical appraisal: A primer for headache clinicians and researchers.

The goal is to provide an overview of artificial intelligence (AI) and machine learning (ML) methodology and appraisal tailored to clinicians and researchers in the headache field to facilitate interdisciplinary communications and research. The application of AI to the study of headache and other healthcare challenges is growing rapidly. It is critical that these findings be accurately interpreted by headache specialists, but this can be difficult for non-AI specialists. This paper is a narrative review of the fundamentals required to understand ML/AI headache research. Using guidance from key leaders in the field of headache medicine and AI, important references were reviewed and cited to provide a comprehensive overview of the terminology, methodology, applications, pitfalls, and bias of AI. We review how AI models are created, common model types, methods for evaluation, and examples of their application to headache medicine. We also highlight potential pitfalls relevant when consuming AI research, and discuss ethical issues of bias, privacy and abuse generated by AI. Additionally, we highlight recent related research from across headache-related applications. Many promising current and future applications of ML and AI exist in the field of headache medicine. Understanding the fundamentals of AI will allow readers to understand and critically appraise AI-related research findings in their proper context. This paper will increase the reader's comfort in consuming AI/ML-based research and will prepare them to think critically about related research developments.