Effects Of Different Models Research Articles

Mechanical properties and global warming potential predictions for high-performance concrete through machine learning models integrated with meta-heuristic algorithms

Recent developments in high-performance concrete (HPC) have made it a high-tech material with enhanced durability and properties, and it is a more ecological material with a longer life cycle. Conventional approaches to assessing HPC properties and environmental effects may lack precision in predicting the effects of additional cementitious materials. Adaptive Boosting Algorithm (ADA) and Stochastic Gradient Boosting (SGB) have emerged as promising approaches for predicting HPC properties and environmental effects, offering more accurate and efficient alternative methods. In this study, the ADA and SBG models have been employed to predict the two essential HPC mechanical properties, including compressive and tensile strengths (CS and TS) and Global Warming Potential (GWP) by coupling with meta-heuristic algorithms, namely Stadium Spectators Optimizer (SSO), Golden Jackal Algorithm (GJA), Brown-bear Optimization Algorithm (BOA), and Golf Optimization Algorithm (GOA)‎ for developing hybrid and ensemble prediction approaches. The study assessed the effectiveness of different models for predicting HPC properties and GWP by employing various metrics. The SGSBGG framework showcased remarkable precision and performance compared to other models in the prediction of CS, TS, and GWP. It outshone its counterparts by achieving the highest prediction accuracy, securing R2 values of 0.994, 0.994, and 0.995 for CS, TS, and GWP predictions, respectively. Additionally, it demonstrated unparalleled accuracy with the lowest modeling errors, registering an RMSE of 1.306 for CS, 0.097 for TS, and 4.469 for GWP. Despite the clear superiority of the SGSBGG model, it is noteworthy that the SGSO, as a hybrid model, delivered notably compatible results.

Enhancing energy hub efficiency through advanced modelling and optimization techniques: A case study on micro-refinery output products and parking lot integration

A new model of energy carriers (micro-refinery output products) in the concept of an energy hub is presented. In addition, in the presented model, the effect of different models of parking lot in an energy hub is analyzed. In this study, the uncertainty of the number of electric vehicles was modeled using the Monte Carlo method, and then considering the same conditions, the uncertainty of the number of electric vehicles was calculated using the Probability-Possibility hybrid method. In addition, the study uses a scenario-based approach to address uncertainties related to multi-carrier energy demand and multi-carrier energy prices. In the present paper, ensuring water demand is of great importance, which is why the proposed energy hub structure of a sea desalination unit and sour water treatment that is extracted from the micro refinery output was analyzed. The optimization model presented in this paper for the energy hub is a complex integer linear programming (MILP) that is solved using a CPLEX solver in the GAMS environment. The results show that the use of the Probability-Possibility method resulted in an 8 % reduction in the final energy supply cost in the energy hub compared to the Monte Carlo method.

Implementation of English Language Competence –Based Curriculum in Tanzanian Secondary Schools: A Case Mtama District

The study assessed the implementation of the English language Competence Based Curriculum (CBC) in Tanzanian secondary schools, with a focus on Mtama District. The study employed the Communicative Competence Model (CCM) to guide the study. Data were collected in Mtama District in April 2023, where the study used a mixed methods design involving random and purposeful sampling. The study focused on 21 English teachers and 380 students, employing a complementary research design that integrated qualitative methods such as interviews with quantitative techniques like Likert scale questionnaires. The findings revealed that English teachers in Mtama District implement CBC at a medium level; the study sheds light on the practical aspects of CBC performance, providing an understanding of approaches, strategies, and methods. The study concludes that the successful implementation of the CBC is a collective responsibility, and it is crucial for English teachers to be proactive in their training and development. The study also recommends the effectiveness of different models of English teacher training and professional development programs, specifically to CBC methodologies and innovative teaching strategies and pedagogical approaches that enhance the integration of CBC principles in English language teaching.

Dynamic viscoelastic properties of ultra-large particle size asphalt mixture based on fractional derivative constitutive model

The aim of this paper was to accurately grasp and precisely characterize the dynamic viscoelastic properties of Ultra-Large Particle Size Asphalt Mixture (LSAM-50), and fully explore the features and simplified forms of the generalized fractional derivative Zener (GFDZ) model. The constitutive relations of the GFDZ model with n Abel elements were first given, and a modified GFDZ (mGFDZ) model was proposed. Then the master curves of LSAM-50, AC-13 and AC-20 mixtures were constructed, and the expression effects of different models and the dynamic viscoelastic behaviors of different asphalt mixtures were compared. The results showed that LSAM-50 was a typical viscoelastic material similar to traditional mixture. Both dynamic modulus and energy storage modulus decreased with increasing temperature. The phase angle increased first and then decreased with temperature increasing, and the peak value appeared near 40℃ at low frequency. The loss modulus increased first and then decreased with the temperature increasing. When the loading frequency was 0.1 Hz∼25 Hz, the peak value appeared at 0℃∼20℃. The rutting factors of LSAM-50 became larger with temperature reduction and frequency enhancement. When the number of Abel elements reached 2 in the GFDZ model or reached 3 in the mGFDZ model, the model fitting effects would no longer change significantly. The 2-mGFDZ model with only 5 parameters fitted master curves data satisfactorily and the physical meanings of parameters were clear. Comparing among three mixtures, LSAM-50, with the lowest asphalt content, showed significantly superior elasticity and better deformation resistance at medium and high temperatures. At low temperatures, the viscosity behavior of LSAM-50 was slightly inferior, but it may have pronounced plate properties considering the effect of aggregate skeletonization. The research results would promote new pavement structural design and mechanical response analysis.

Emotion Detection from EEG Signals Using Machine Deep Learning Models.

Detecting emotions is a growing field aiming to comprehend and interpret human emotions from various data sources, including text, voice, and physiological signals. Electroencephalogram (EEG) is a unique and promising approach among these sources. EEG is a non-invasive monitoring technique that records the brain's electrical activity through electrodes placed on the scalp's surface. It is used in clinical and research contexts to explore how the human brain responds to emotions and cognitive stimuli. Recently, its use has gained interest in real-time emotion detection, offering a direct approach independent of facial expressions or voice. This is particularly useful in resource-limited scenarios, such as brain-computer interfaces supporting mental health. The objective of this work is to evaluate the classification of emotions (positive, negative, and neutral) in EEG signals using machine learning and deep learning, focusing on Graph Convolutional Neural Networks (GCNN), based on the analysis of critical attributes of the EEG signal (Differential Entropy (DE), Power Spectral Density (PSD), Differential Asymmetry (DASM), Rational Asymmetry (RASM), Asymmetry (ASM), Differential Causality (DCAU)). The electroencephalography dataset used in the research was the public SEED dataset (SJTU Emotion EEG Dataset), obtained through auditory and visual stimuli in segments from Chinese emotional movies. The experiment employed to evaluate the model results was "subject-dependent". In this method, the Deep Neural Network (DNN) achieved an accuracy of 86.08%, surpassing SVM, albeit with significant processing time due to the optimization characteristics inherent to the algorithm. The GCNN algorithm achieved an average accuracy of 89.97% in the subject-dependent experiment. This work contributes to emotion detection in EEG, emphasizing the effectiveness of different models and underscoring the importance of selecting appropriate features and the ethical use of these technologies in practical applications. The GCNN emerges as the most promising methodology for future research.

Distinct biophysiological effects of Ramadan fasting and traditional intermittent fasting on markers of body fat storage. A real-life study

BackgroundRamadan Intermittent fasting (RIF) exerts beneficial metabolic effects and improves gastrointestinal motility. However, a comparison between RIF and the traditional 16-hours intermittent fasting (16IF), a strategy for weight loss, is lacking. MethodsA total of 34 subjects (median age 32.5 years, range 18–63 years; median BMI 24.5 Kg m-1², range 18.6–37.6 Kg m-1²) were assigned to RIF (N = 18) or 16IF (N = 16) for 30 days. We measured variations in anthropometric measures (BMI, waist, and abdominal circumference), serum insulin, glucose, cortisol, non-esterified fatty acid (NEFA), body fat composition (bioelectrical impedance analysis), and the ultrasonographic measurements of liver steatosis (Hepatorenal index, HRI) and thickness of subcutaneous (SAT) and visceral (VAT) fats. ResultsAt baseline, BMI, rates of liver steatosis and distribution of normal weight, overweight, and obese subjects were comparable between the two groups. Body weight significantly decreased at the end of fasting in both RIF (-4.2 % vs baseline, P = 0.002) and 16IF (-2.1 % vs baseline, P = 0.002). Waist and abdominal circumferences significantly decreased only in RIF as well as the amount of body fat. In subjects with liver steatosis, SAT and VAT significantly decreased following RIF, but not 16h-IF, as well as the ultrasonographic HRI. ConclusionBoth 16IF and RIF are able, during 1-month, to reduce body weight. However, RIF but not 16IF also generates marked beneficial effects in terms of reduced subcutaneous fat and liver steatosis. Further studies urge to verify the effects of different models of IF in weight-cycling and long-term management of obesity and related dysmetabolic conditions, such as ectopic fat over-storage.

Biomechanical effects of different instrumented segments and trunk shifts on distal adjacent segments after congenital scoliosis posterior hemivertebrectomy: Preliminary results of a single case

ObjectiveThe present study aims to discuss the biomechanical effects of the sagittal vertical axis and different instrumented segments on distal adjacent segments after congenital scoliosis posterior hemivertebrectomy. MethodA case of congenital scoliosis caused by hemivertebra was selected for the reconstruction of the preoperative and postoperative 3D computed tomography data of the full spine. A finite element model of different fusion lengths and postoperative trunk shift (TS) values was established using the finite element method to compare the biomechanical effects of different models on the distal adjacent segment. ResultIn the L1–L3 and T12–L1–L3–L4 fusion modes, the horizontal shift of the 1st vertebra below the lowest instrumented vertebra (LIV) increased with the trunk shift (TS) expansion after operation, and the imbalance between the left and right vertical stress of the 1st intervertebral disc below the LIV increased. With the decrease in fused segments in cases of TS = 10 mm and TS = 5 mm, the 1st vertebra below the LIV was subjected to a greater unbalanced force in the horizontal direction, and the 1st intervertebral disc below the LIV was subjected to a smaller imbalance between the left and right vertical stress after operation. ConclusionWhen treating congenital scoliosis with hemivertebrectomy and pedicle screw fixation, fused segments can be properly extended and the postoperative TS shortened with a view of reducing the imbalance between the left and right stress of the 1st intervertebral disc below the LIV as well as the horizontal shift of the 1st vertebra below the LIV.

From Detection to Application: Recent Advances in Understanding Scientific Tables and Figures

Tables and figures are usually used to present information in a structured and visual way in scientific documents. Understanding the tables and figures in scientific documents is significant for a series of downstream tasks, such as academic search, scientific knowledge graphs, and so on. Existing studies mainly focus on detecting figures and tables from scientific documents, interpreting their semantics, and integrating them into downstream tasks. However, a systematic and comprehensive literature review on the mining and application of tables and figures in academic papers is still missing. In this article, we introduce the research framework and the whole pipeline for understanding tables and figures, including detection, structural analysis, interpretation, and application. We deliver a thorough analysis of benchmark datasets, recent techniques, and their pros and cons. Additionally, a quantitative analysis of the effectiveness of different models on popular benchmarks is presented. We further outline several important applications that exploit the semantics of scientific tables and figures. Finally, we highlight the challenges and some potential directions for future research. We believe this is the first comprehensive survey in understanding scientific tables and figures that covers the landscape from detection to application.

Alantolactone attenuates high-fat diet-induced inflammation and oxidative stress in non-alcoholic fatty liver disease

BackgroundNonalcoholic fatty liver disease (NAFLD) is a chronic disease with an increasing incidence, which can further develop into liver fibrosis and hepatocellular carcinoma at the end stage. Alantolactone (Ala), a sesquiterpene lactone isolated from Asteraceae, has shown anti-inflammatory effects in different models. However, the therapeutic effect of Ala on NAFLD is not clear.MethodsC57BL/6 mice were fed a high-fat diet (HFD) to induce NAFLD. After 16 weeks, Ala was administered by gavage to observe its effect on NAFLD. RNA sequencing of liver tissues was performed to investigate the mechanism. In vitro, mouse cell line AML-12 was pretreated with Ala to resist palmitic acid (PA)-induced inflammation, oxidative stress and fibrosis.ResultsAla significantly inhibited inflammation, fibrosis and oxidative stress in HFD-induced mice, as well as PA-induced AML-12 cells. Mechanistic studies showed that the effect of Ala was related to the induction of Nrf2 and the inhibition of NF-κB. Taken together, these findings suggested that Ala exerted a liver protective effect on NAFLD by blocking inflammation and oxidative stress.ConclusionsThe study found that Ala exerted a liver protective effect on NAFLD by blocking inflammation and oxidative stress, suggesting that Ala is an effective therapy for NAFLD.

Different Models of Cardiac Telerehabilitation for People with Coronary Artery Disease: Features and Effectiveness: A Systematic Review and Meta-Analysis.

Objectives: Cardiac telerehabilitation (TR) for coronary artery disease (CAD) is a feasible alternative to the center-based rehabilitation delivery model. However, the features of exercise-based cardiac TR are still heterogeneous among studies, making it difficult to disentangle the preferable reference strategies to be recommended for the adoption of this new delivery of care. In addition, little is known about the effectiveness of different models, such as the hybrid model (CRh) including both center-based and home-based telerehabilitation approaches, and the solely home-based telerehabilitation (CTR). Methods: We conducted a systematic review of randomized controlled trials (RCTs) that included TR intervention in patients with CAD to profile the features of the telerehabilitation approach for CAD. We also conducted a meta-analysis to separately assess the effectiveness of CTR and CRh on medical benefit outcome measures compared to conventional intervention (CI). Results: Out of 17.692 studies, 28 RCTs involving 2.662 CAD patients were included in the review. The studies presented an equal proportion of the CTR and CRh models. The interventions were mainly multidimensional, with a frequency of 1 month to 6 months, with each session ranging between 20 to 70 min. In CRh, the intervention was mainly consecutive to center-based rehabilitation. All studies adopted asynchronous communication in TR, mainly providing monitoring/assessment, decisions, and offline feedback. Few studies reported mortality, and none reported data about re-hospitalization or morbidity. Adherence to the CTR and CRh interventions was high (over 80%). The meta-analyses showed the superior effect of CTR compared to CI in exercise capacity. An overall noninferiority effect of both CTR and CRh compared to CI was found with factors including risk control and participation. Conclusions: The results of the review and meta-analyses indicated that CTR and CRh are equally effective, safe, convenient, and valid alternatives to cardiac conventional interventions. The evidence suggests that telerehabilitation may represent a valid alternative to overcome cardiac rehabilitation barriers.

Antioxidants, Hormetic Nutrition, and Autism.

Autism spectrum disorder (ASD) includes a heterogeneous group of complex neurodevelopmental disorders characterized by atypical behaviors with two core pathological manifestations: deficits in social interaction/communication and repetitive behaviors, which are associated with disturbed redox homeostasis. Modulation of cellular resilience mechanisms induced by low levels of stressors represents a novel approach for the development of therapeutic strategies, and in this context, neuroprotective effects of a wide range of polyphenol compounds have been demonstrated in several in vitro and in vivo studies and thoroughly reviewed. Mushrooms have been used in traditional medicine for many years and have been associated with a long list of therapeutic properties, including antitumor, immunomodulatory, antioxidant, antiviral, antibacterial, and hepatoprotective effects. Our recent studies have strikingly indicated the presence of polyphenols in nutritional mushrooms and demonstrated their protective effects in different models of neurodegenerative disorders in humans and rats. Although their therapeutic effects are exerted through multiple mechanisms, increasing attention is focusing on their capacity to induce endogenous defense systems by modulating cellular signaling processes such as nuclear factor erythroid 2 related factor 2 (Nrf2) and nuclear factor-kappa B (NF-κB) pathways. Here we discuss the protective role of hormesis and its modulation by hormetic nutrients in ASD.

Eigenvector spatial filtering enhancing natural hazards vulnerability assessment in a susceptible urban environment: A case study of Izmir earthquake in Turkey

The increasing risk of earthquakes in urban areas has made it crucial to develop accurate vulnerability models for city infrastructure and systems. We aimed to assess and compare the effectiveness of different models and vulnerability analysis techniques in predicting earthquake vulnerability in the specific context of Izmir, Turkey. One central hypothesis in this research aimed to determine whether integrating Eigenvector Spatial Filtering (ESF) into both regression models and machine learning algorithms would yield a comparable enhancement in model performance. We performed earthquake vulnerability modeling (EVM) by considering (ⅰ) only seismic-related variables (SRV) and (ⅱ) integrating ESF by using Moran's eigenvector maps (MEMs). For each approach, we evaluated the predictive performance of two simple regression-based models; generalized linear model (GLM) and generalized additive model (GAM), and two complex machine learning ones; generalized boosting model (GBM), and random forest (RF). The study utilized five primary indicators encompassing geotechnical, physical, structural, social, and facilities data. The predictive performance of the models was assessed using evaluation metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and adjusted R2. The results indicated that the optimal candidate model consisted of five key variables: altitude, building height, distance to safety gathering places, Peak Ground Acceleration (PGA), and population density. We found that decision-tree-based methods performed better than regression-based methods for both modeling schemes. RF exhibited the highest predictive performance for the training data (RMSE = 0.59, adjusted R2 = 0.71), while GBM outperformed other models for the test data (RMSE = 0.79, adjusted R2 = 0.78). However, incorporating ESF to the EVM analysis revealed that regression-based methods, particularly the GLM, obtained highest improvement in accuracy (RMSE 0.94 vs 0.76 and adjusted R2 0.56 vs 0.71 for the SRV and SRV + MEMs modeling approach). Significant differences were observed between GLM-GBM and GLM-RF comparisons, as well as GAM-GBM and GAM-RF comparisons. The findings of this research are expected to be helpful for informed decision-making, targeted risk reduction, and the development of effective policies and strategies to enhance preparedness and resilience in the face of seismic events in highly susceptible urban systems.

Comparison and investigation on characteristics of polycystic ovary syndrome rat models induced by letrozole, testosterone propionate, and high-fat diets

Research QuestionWhat are the long-term effects of different models of polycystic ovary syndrome (PCOS), and which model could be used in future research? DesignPCOS models induced by letrozole (LE), LE and high-fat diets (HFD), testosterone propionate (TP), or TP and HFD were established respectively. Body weight, energy intake, blood glucose, sex hormone levels, lipid profiles, and estrous cycle were observed. Histology of ovaries, large intestine, and fat were displayed by Hematoxylin-eosin or Oil red O staining. The protein and mRNA levels of the hormone synthesis, oocyte maturation, gut barrier, lipid metabolism, and inflammation were evaluated using western blot, immunohistochemistry, and polymerase chain reaction. Microbial community composition was measured by 16S RNA sequencing. ResultsLE treatment induced hyperandrogenemia, polycystic ovary morphology, disrupted estrous cycle, and impaired ovarian function. However, these effects could be restored within 42 days. Concurrently, LE enhanced excess fat storage, diminished adipocyte browning and energy expenditure, developed insulin resistance, affected the inflammation state, and compromised the intestinal barrier. HFD could amplify the metabolic disturbance. While the pituitary-ovarian axis was more efficiently and consistently affected by TP. TP also disturbed intestinal microenvironment. However, the effects on metabolism were not as profound as LE, which can be supplemented by HFD. ConclusionsLE is beneficial for studies on PCOS metabolic disturbances; LE+HFD is suitable for investigations on PCOS metabolic abnormalities and the gut-PCOS link. Whereas, T injection is appropriate for studying PCOS reproductive abnormalities; T+HFD treatment is the most comprehensive for studying PCOS reproductive abnormalities, metabolic disturbances, and the gut-PCOS link.

Machine Learning-Based Player Performance Analysis for Association of Tennis Professionals Tour

This study explores the use of data analysis in professional tennis, using the dataset of the world-renowned Association of Tennis Professionals (ATP) Tour, which is the top-class men's professional tennis tour. Utilizing the match data of all the players, mainly Novak Djokovic, the research applies various models and methods of data preprocessing and selection to predict match outcomes. The main objective is to provide data-supported insight to professional tennis players and coaches to help analyze and further improve their tennis. The analysis and model take factors like player performance, court types, and tournament importance into consideration. This study shows the potential of data analysis in sports where such resources are often limited to well-funded teams. Not only the effect of different models in forecasting tennis match outcomes is evaluated, but also the influence of non-critical tournaments and external factors like injuries and policies on the accuracy of predictions are explored. The research findings offer insights into the evolving role of data analysis in enhancing sports strategies and performance, indicating a promising future for data-driven decision-making in professional tennis for different levels of players.

AI-Based Holistic Model for Decision-Support System in Inventory Optimization using deep Learning

Abstract: This study introduces an innovative artificial intelligence-driven comprehensive model designed for a decision-support system in inventory optimization. This model was developed using advanced deep learning techniques. The model aims to surpass the constraints of typical inventory management systems by effectively capturing temporal correlations and nuanced patterns in the data. This is achieved by integrating Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures. To navigate modern supply chains effectively, the primary focus should be on offering real-time decision support. The approach aims to enhance the decision-making processes in inventory management. This brief introduction offers insight into a distinctive method aimed at addressing the challenges of modern supply networks. The strategy focuses on providing realtime decision support and optimization strategies to enhance inventory management efficiency. The model's effectiveness is demonstrated by thorough performance evaluations, showing higher accuracy, precision, and recall compared to existing logistic regression models. The suggested LSTM model achieves an accuracy of 0.99, precision of 0.96, recall of 0.94, and an F1 Score of 0.97, surpassing existing models with higher scores in all metrics. The GRU model, albeit somewhat less effective than the LSTM, shows great overall performance, especially in recall with a score of 0.97. This investigation compares the effectiveness of different models, focusing on the potential advancements made possible by LSTM and GRU architectures in improving decision-support systems for inventory optimization. The AI-Based Holistic Model provided is at the forefront of technology advancements, offering a comprehensive solution for making educated decisions and optimizing inventories sustainably in today's highly competitive corporate climate.

Stock price prediction of PayPal by Linear Regression, SVM, Random Forest and LSTM

This research paper undertakes an extensive and detailed examination of four distinct machine learning models, specifically Linear Regression, Support Vector Machine (SVM), Random Forest, and Long Short-Term Memory (LSTM). The stock values of PayPal are forecast employing these techniques, and Python serves as the fundamental tool in facilitating a comprehensive and thorough evaluation of their efficacy in predicting trends in the financial market. In the realm of methodology, this study encompasses a multifaceted approach. Python forms the cornerstone for data analysis, model development, and testing. The data collection process encompasses historical stock price data for PayPal, alongside an array of relevant economic indicators. The core of the study lies in the comparative analysis of the four machine learning models. Each model is rigorously tested against historical data, allowing for a nuanced understanding of their strengths and weaknesses. Linear Regression, remarkably, emerges as the standout performer in terms of predictive accuracy and consistency. This firmly establishes Linear Regression as the optimal choice for forecasting stock prices. The significance distinction of this research extends beyond its findings. It advances the discipline of financial forecasting by illuminating the comparative effectiveness of different models, offering valuable insights for guiding future research initiatives within this domain. The integration of advanced methodologies and the clear-cut conclusion regarding Linear Regression's superiority underscore the pivotal role of this study in enhancing the precision of financial market trend forecasting.

Pharmacokinetic Effects of Different Models of Nonalcoholic Fatty Liver Disease in Transgenic Humanized OATP1B Mice.

Organic anion transporting polypeptide (OATP) 1B1 and OATP1B3 (collectively, OATP1B) transporters encoded by the solute carrier organic anion transporter (SLCO) genes mediate uptake of multiple pharmaceutical compounds. Nonalcoholic steatohepatitis (NASH), a severe form of nonalcoholic fatty liver disease (NAFLD), decreases OATP1B abundance. This research characterized the pathologic and pharmacokinetics effects of three diet- and one chemical-induced NAFLD model in male and female humanized OATP1B mice, which comprises knock-out of rodent Oatp orthologs and insertion of human SLCO1B1 and SLCO1B3. Histopathology scoring demonstrated elevated steatosis and inflammation scores for all NAFLD-treatment groups. Female mice had minor changes in SLCO1B1 expression in two of the four NAFLD treatment groups, and pitavastatin (PIT) area under the concentration-time curve (AUC) increased in female mice in only one of the diet-induced models. OATP1B3 expression decreased in male and female mice in the chemical-induced NAFLD model, with a coinciding increase in PIT AUC, indicating the chemical-induced model may better replicate changes in OATP1B3 expression and OATP substrate disposition observed in NASH patients. This research also tested a reported multifactorial pharmacokinetic interaction between NAFLD and silymarin, an extract from milk thistle seeds with notable OATP-inhibitory effects. Males showed no change in PIT AUC, whereas female PIT AUC increased 1.55-fold from the diet alone and the 1.88-fold from the combination of diet with silymarin, suggesting that female mice are more sensitive to pharmacokinetic changes than male mice. Overall, the humanized OATP1B model should be used with caution for modeling NAFLD and multifactorial pharmacokinetic interactions. SIGNIFICANCE STATEMENT: Advanced stages of NAFLD cause decreased hepatic OATP1B abundance and increase systemic exposure to OATP substrates in human patients. The humanized OATP1B mouse strain may provide a clinically relevant model to recapitulate these observations and predict pharmacokinetic interactions in NAFLD. This research characterized three diet-induced and one drug-induced NAFLD model in a humanized OATP1B mouse model. Additionally, a multifactorial pharmacokinetic interaction was observed between silymarin and NAFLD.

Tensile and Compressive Properties of Woven Fabric Carbon Fiber-Reinforced Polymer Laminates Containing Three-Dimensional Microvascular Channels.

Microvascular self-healing composite materials have significant potential for application and their mechanical properties need in-depth investigation. In this paper, the tensile and compressive properties of woven fabric carbon fiber-reinforced polymer (CFRP) laminates containing three-dimensional microvascular channels were investigated experimentally. Several detailed finite element (FE) models were established to simulate the mechanical behavior of the laminate and the effectiveness of different models was examined. The damage propagation process of the microvascular laminates and the influence of microvascular parameters were studied by the validated models. The results show that microvascular channels arranged along the thickness direction (z-direction) of the laminates are critical locations under the loads. The channels have minimal effect on the stiffness of the laminates but cause a certain reduction in strength, which varies approximately linearly with the z-direction channel diameter within its common design range of 0.1~1 mm. It is necessary to consider the resin-rich region formed around microvascular channels in the warp and weft fiber yarns of the woven fabric composite when establishing the FE model. The layers in the model should be assigned with equivalent unidirectional ply material in order to calculate the mechanical properties of laminates correctly.

Exploring the Cognitive Neural Basis of Factuality in Abstractive Text Summarization Models: Interpretable Insights from EEG Signals

(1) Background: Information overload challenges decision-making in the Industry 4.0 era. While Natural Language Processing (NLP), especially Automatic Text Summarization (ATS), offers solutions, issues with factual accuracy persist. This research bridges cognitive neuroscience and NLP, aiming to improve model interpretability. (2) Methods: This research examined four fact extraction techniques: dependency relation, named entity recognition, part-of-speech tagging, and TF-IDF, in order to explore their correlation with human EEG signals. Representational Similarity Analysis (RSA) was applied to gauge the relationship between language models and brain activity. (3) Results: Named entity recognition showed the highest sensitivity to EEG signals, marking the most significant differentiation between factual and non-factual words with a score of −0.99. The dependency relation followed with −0.90, while part-of-speech tagging and TF-IDF resulted in 0.07 and −0.52, respectively. Deep language models such as GloVe, BERT, and GPT-2 exhibited noticeable influences on RSA scores, highlighting the nuanced interplay between brain activity and these models. (4) Conclusions: Our findings emphasize the crucial role of named entity recognition and dependency relations in fact extraction and demonstrate the independent effects of different models and TOIs on RSA scores. These insights aim to refine algorithms to reflect human text processing better, thereby enhancing ATS models’ factual integrity.

Pattern recognition study of musical rhythm perception based on multimodal deep learning

Abstract Rhythm perception is becoming more and more important in the field of music information processing and music understanding. The study first adopts signal processing methods to extract musical features, then uses feature fusion techniques to integrate features of different modalities into a single feature vector. Based on this model, the study identifies the rhythmic activation function of music and combines it with the hidden Markov model to infer the rhythm of the music. One of the key points of the study is to perform rhythm recognition on music containing drums, to explore the recognition effect. One of the focuses of the study is to recognize the rhythm of music containing drums to explore the recognition effect.In addition, the study also analyzes the Softmax output values of the music and compares the recognition effect of different models. The results show that the rhythm recognition of music using the multimodal deep learning method performs the best in terms of the F-Measure value, the Cemgil value, the Goto value, and the P-score value, with the respective 65.65%, 66.76%, 36.75%, and 36.75%. 66.76%, 36.75%, and 75.68%.Especially in the drum music recognition, the position of each drum music is accurately recognized, proving the model’s effectiveness in this paper. The research provides a new feasible method for the recognition and understanding of music rhythms and a valuable reference for the research in this field.