Analysis Model Research Articles

Influence of prosthetic index structures and implant materials on stress distribution in implant restorations: a three-dimensional finite element analysis

BackgroundMechanical complications affect the stability of implant restorations and are a key concern for clinicians, especially with the frequent introduction of new implant designs featuring various structures and materials. This study evaluated the effect of different prosthetic index structure types and implant materials on the stress distribution of implant restorations using both in silico and in vitro methods.MethodsFour finite element analysis (FEA) models of implant restorations were created, incorporating two prosthetic index structures (cross-fit (CF) and torc-fit (TF)) and two implant materials (titanium and titanium-zirconium). A static load was applied to each group. An in vitro study using digital image correlation (DIC) with a research scenario identical to that of the FEA was conducted for validation. The primary strain, sensitivity index, and equivalent von Mises stress were used to evaluate the outcomes.ResultsChanging the implant material from titanium to titanium-zirconium did not significantly affect the stress distribution or maximum stress value of other components, except for the implant itself. In the CF group, implants with a lower elastic modulus increased the stress on the screw. The TF group showed better stress distribution on the abutment and a lower stress value on the screw. The TF group demonstrated similar sensitivity for all components. DIC analysis revealed significant differences between TF-TiZr and CF-Ti in terms of the maximum (P < 0.001) and minimum principal strains (P < 0.05) on the implants and the minimum principal strains on the investment materials in both groups (P < 0.001).ConclusionsChanges in the implant material significantly affected the maximum stress of the implant. The TF group exhibited better structural integrity and reliability.

The eco-efficiency of fisheries in EU countries

The main goal of this article was to (1) assess the dynamics of eco-efficiency of fisheries in EU countries and its components and (2) identify potential sources of inefficiencies and efficiency surpluses through slack analysis. The hybrid data envelopment analysis (DEA) model was used for the 2008-2019 period. Progress in eco-efficiency was found among 11 countries (out of 23), but the average eco-efficiency index for the sample was 0.988. Differences in the levels and dynamics of eco-efficiency between the studied countries were mainly driven by the efficiency change component, i.e. internal factors. The largest input-saving potential was found in relation to number of employees and gross tonnage of the vessel, suggesting that sample countries deal with the problem of overinvestment and overstaffing. We also found that greenhouse gas emissions could be reduced by approximately a third.

Investigating Combination Therapy: The Role of Lutetium-177 PSMA-617 Radioligand Therapy and Androgen Receptor Pathway Inhibitors in Metastatic Castration-Resistant Prostate Cancer.

Background: The combination of Lutetium-177 (Lu-177) PSMA-617 radioligand therapy (RLT) with androgen receptor pathway inhibitors (ARPIs) has shown promise in metastatic castration-resistant prostate cancer (mCRPC). However, real-world data on the efficacy and safety of this combination are limited. This study aimed to evaluate the impact of combination therapy with Lu-177 PSMA-617 RLT and ARPIs on progression-free survival (PFS) and overall survival (OS) in patients with mCRPC. Methods: In this retrospective study, 104 mCRPC patients receiving Lu-177 PSMA-617 RLT at our institution between December 2017 and January 2024 were divided into the following two groups those receiving Lu-177 PSMA-617 RLT plus ARPI (n = 34) and those receiving Lu-177 PSMA-617 RLT alone (n = 70). Patients received 150 to 200 millicuries Lu-177 PSMA-617 RLT in each cycle. PFS and zOS were assessed using Kaplan-Meier analysis and Cox proportional hazard models. Results: The combination therapy significantly prolonged median PFS compared to Lu-177 PSMA-617 RLT alone (11 vs. 5.6 months; HR, 0.47; 95% CI, 0.28-0.79; p < 0.01). A trend towards improved OS was also observed in the combination group (20.3 vs. 15.9 months; HR, 0.58; 95% CI, 0.33-1.02; p = 0.06). Age was a significant predictor of OS (21.2 vs. 12.4 months for younger vs. older patients; p < 0.01), while Gleason score and visceral involvement did not significantly impact PFS. The safety profile indicated that adverse effects were generally comparable between the two groups, with no statistically significant differences in the incidence of anemia, neutropenia, thrombocytopenia, nephrotoxicity, or hepatotoxicity. Conclusions: This study provides evidence that combining Lu-177 PSMA-617 RLT with ARPIs may significantly improve PFS in mCRPC patients. The potential OS benefit warrants further investigation in larger prospective trials. Age should be considered when making treatment decisions for mCRPC patients.

Causation Correlation Analysis of Aviation Accidents: A Knowledge Graph-Based Approach

Summarizing the causation of an aviation accident is beneficial for improving aviation safety. Currently, accident analysis mainly focuses on causal analysis, while giving less consideration to the correlation between accident causal factors and other accident factors. To clarify accident causal factors and potential patterns affecting aviation safety and to optimize data mining methods for accident causal factors, this work proposes an aviation accident causation correlation analysis model based on a knowledge graph. Firstly, the accident causal factors are identified, and a knowledge graph is constructed. Subsequently, by utilizing multi-dimensional topological analysis metrics, an aviation accident causation correlation analysis model is established, using the relationships within accident causal factors as a foundation, to determine potential patterns among accident causal factors, flight phases, accident types, and consequences and to analyze the key accident causal factors influencing accident occurrences across different flight phases. Finally, preventive measures and recommendations are provided based on the analysis conclusions. Through a case study using 437 global aviation accidents from 2018 to 2022 as samples and employing the knowledge graph-based aviation accident causation correlation analysis model, the causation relationships among accident causal factors can be expressed more clearly, the potential risks of various accident causal factors can be identified, experiences can be gained from historical accident data, and underlying patterns can be unearthed. This work can provide auxiliary decision making and be an effective reference for the prevention of aviation accidents, playing a positive role in enhancing the level of aviation safety management.

Rethinking time-lagged emissions and abatement potential of fluorocarbons in the post-Kigali Amendment era

The Montreal Protocol has been successful in safeguarding the ozone layer and curbing climate change. However, accurately estimating and reducing the time-lagged emissions of ozone-depleting substances or their substitutes, such as produced but not-yet-emitted fluorocarbon banks, remains a significant challenge. Here, we use a dynamic material flow analysis model to characterize the global stocks and flows of two fluorocarbon categories, hydrochlorofluorocarbons (HCFCs) and hydrofluorocarbons (HFCs), from 1986 to 2060. We assess emission pathways, time-lagged emission sizes, and potential abatement measures throughout different life cycle stages while focusing on the role of banked fluorocarbons in global and regional decarbonization efforts in the post-Kigali Amendment era. Although fluorocarbon releases are expected to decline, the cumulative global warming potential (GWP)-weighted emissions of HCFCs and HFCs are significant; these will be 6.4 (±1.2) and 14.8 (±2.5) gigatons CO2-equivalent, respectively, in 2022–2060 in our business-as-usual (BAU) scenario. Scenario analysis demonstrates that implementing currently available best environmental practices in developed economies can reduce cumulative GWP-weighted emissions by up to 45% compared with the BAU scenario.

An experimental analysis and deep learning model to assess the cooling performance of green walls in humid climates

Introduction: Amidst escalating global temperatures, increasing climate change, and rapid urbanization, addressing urban heat islands and improving outdoor thermal comfort is paramount for sustainable urban development. Green walls offer a promising strategy by effectively lowering ambient air temperatures in urban environments. While previous studies have explored their impact in various climates, their effectiveness in humid climates remains underexplored.Methods: This research investigates the cooling effect of a green wall during summer in a humid climate, employing two approaches: Field Measurement-Based Analysis (SC 1: FMA) and Deep Learning Model (SC 2: DLM). In SC 1: FMA, experiments utilized data loggers at varying distances from the green wall to capture real-time conditions. SC 2: DLM utilized a deep learning model to predict the green wall’s performance over time.Results: Results indicate a significant reduction in air temperature, with a 1.5°C (6%) decrease compared to real-time conditions. Long-term analysis identified specific distances (A, B, C, and D) contributing to temperature reductions ranging from 1.5°C to 2.5°C, highlighting optimal distances for green wall efficacy.Discussion: This study contributes novel insights by determining effective distances for green wall systems to mitigate ambient temperatures, addressing a critical gap in current literature. The integration of a deep learning model enhances analytical precision and forecasts future outcomes. Despite limitations related to a single case study and limited timeframe, this research offers practical benefits in urban heat island mitigation, enhancing outdoor comfort, and fostering sustainable and climate-resilient urban environments.

Seismic response of column‐supported silos considering granular–structure interaction

AbstractTo predict the seismic response of column‐supported silos (CSSs), the granular–structure interaction (GSI) analysis method is proposed with considering the combined effect of the friction between the particles–particles and the particles–silo wall. Using free‐body dynamic equilibrium equations, we reconstruct the mutual interactions between different grain portions and between the grains and the silo wall to develop the ideal calculation model of the CSS structure. Based on the analysis model, additional dynamic overpressure and the effective mass caused by the stored content interacting with the silo wall is obtained with different slenderness ratios and peak accelerations. The additional bending moment caused by the friction between the particles and silo wall is further quantified. To verify the reliability of the proposed method, we discuss some applicative examples by comparing the GSI method with other theories, Eurocode 8, and experimental results. Moreover, the along‐the‐height acceleration profiles of the silo wall and the ensiled content are analyzed according to the shaking‐table tests. The results show that the GSI method can match Janssen's theory well in the case of static pressure at slenderness ratios exceeding 1.0. The overpressure profiles along the height of the silo wall follow a nonlinear distribution, different from Eurocode 8. The bending moment obtained by predictive formulas agrees well with the experimental results for the CSS, indicating that the GSI method is reasonable. Some design and construction recommendations, including the maximum overpressure position, the reference range of the dynamic overpressure coefficient, and the reduction factors of the ensiled content mass, are proposed to facilitate the engineering applications of CSSs, considering different slenderness ratios.

UPDAPS-Flood: a mechanistic-empirical flexible pavement analysis tool to evaluate the effect of flooding events on flexible pavement performance

ABSTRACT Flooding can cause considerable damage to flexible pavements and negatively affects their performance over time. This study aims to evaluate the flooding effects on the flexible pavement performance and the potential resiliency of the pavement network through a mechanistic-empirical (ME) pavement analysis approach. The Unified Pavement Distress Analysis and Prediction System (UPDAPS) programme, which includes ME analysis models, was used to develop the UPDAPS-Flood programme. A total of 7,655 flexible pavement sections were extracted from the Highway Pavement Management System (HPMS) database in three United States regions. Finally, the resiliency of the pavement network to flood events was quantified using the loss of pavement service life based on the international roughness index (IRI). The results showed that flooding has the highest impact on rutting performance, followed by IRI and fatigue cracking. It has been found that a proper drainage system can reduce the flooding impact by a factor of two. The predicted pavement performance was found to be very sensitive to the coefficients of the rutting model, which brings the need for further studies to re-calibrate the rutting prediction model at higher moisture content levels.

The logic behind cyberchondria: Longitudinal relations among risk perception, health anxiety, and online health information seeking

AbstractThe cyberchondria phenomenon presents a significant health concern, yet there remains a relative scarcity of research on the formation mechanisms of cyberchondria and its longitudinal studies. Based on the RISP model and C‐A‐C framework, this study aimed to examine the longitudinal associations between risk perception, health anxiety, and online health information seeking (OHIS) within the context of Chinese social media. We used a three‐wave longitudinal survey with 514 participants at one‐month intervals starting September 2023, employing the Random Intercept Cross‐Lagged Panel Model (RI‐CLPM) for data analysis. At the between‐person level, we solely observed significant negative associations between health anxiety and OHIS. At the within‐person level, (1) positively bidirectional associations were noted among the three main variables; (2) causal relationships were identified between risk perception and OHIS, as well as between health anxiety and OHIS; (3) Health anxiety partially mediated the relationship between risk perception and OHIS, and OHIS fully mediated the relationship between health anxiety at T1 and T3; and (4) multi‐group analyses showed that gender differences in RI‐CLPM were significant, while age differences were insignificant. The results offer theoretical and practical insights for health practitioners, information seekers, and social media platforms.

A Novel Multi-modal Sentiment Analysis Based on Multiple Kernel Learning with Margin-Dimension Constraint

Most of the sentiment analysis studies focus on the sentimental classification of pictures in the video, ignoring the spatio-temporal information of the sequence of picture frames as well as text and audio information. The multiple kernel learning is a new hotspot in the field of nuclear machine learning, capable of handling multiple modalities. For multiple kernel learning, it is easy to ignore the basic features that are not discriminative, and cannot make full use of the base features of different modes. This paper puts forward a novel multi-modal fusion model for sentiment analysis, in which a multiple kernel learning algorithm based on convolution margin-dimension constraint is proposed for feature fusion. Moreover, the 3D convolutional neural network is used to extract the features of visual information, and the multiple kernel learning algorithm based on margin-dimension constraint is used to fuse visual, text and audio sentiment features. Experiments conducted on the MOUD and IEMOCAP sentiment databases show that the proposed model outperforms existing models in the field of multi-modal sentiment analysis research.

Exploring genotype by environment interaction in sunflower using genotype plus genotype by environment interaction (GGE) and best linear unbiased prediction (BLUP) approaches

Sunflower is important for food, feed and ornamentals, faces rising demand for quality oil. Identifying resilient high-yielding genotypes is essential. Multi-environmental trials and integrated approaches to evaluate genotype by environment interactions (GEI) enhance accuracy and comprehensiveness of breeding programs. This study assess the representativeness and discriminative power of tested environments; classify mega-environments for genotype recommendations and evaluate how GGE and BLUP methods identify stable and superior sunflower genotypes. The experiment used a randomized complete block design with three replications across six locations for two seasons. The pooled analysis of variance for GEI on seed yield showed significant variability (p < 0.05) with mean values from 1171 to 2564 kg ha−1, indicating inconsistent genotypes performance across environments. The study identified E11, E2, and E6 as key discriminating and representative environments and grouped test locations into four mega-environments. Genotypes G6, G1, and G4 are the most desirable based on stability and seed yield. E11 and G6 are identified as ideal environment and genotype, respectively. GGE biplot and BLUP methods consistently identify stable and ideal sunflower genotypes. However, GGE stability indices differ somewhat in assessing genotypes stability possibly due to low number of genotypes, testing environment, and year in the dataset. The study confirmed that simultaneous consideration of GGE and BLUP models for GEI analysis helps to select more stable and adaptable sunflower genotypes across environments. It also clarifies GEI pattern, identifying mega-environments, and aiding genotype selection in sunflower production. However, validating these mega-environments and exploring the genetic basis of adaptability and stability remains essential.

Performance Evaluation of Different Clustering Techniques and Parameters of Hybrid PSO- and GA-ANFIS on Optimization and Prediction of Biomethane Yield of Alkali-Pretreated Groundnut Shells

AbstractThe study focuses on optimizing biomethane yield in the anaerobic digestion of alkali-pretreated groundnut shells, involving varied input parameters. Biomethane optimization will improve the economy of the technology, which will assist in managing the environmental challenges of fossil fuel combustion. Traditional methods prove challenging, inaccurate, and uneconomical, necessitating efficient optimization models. This research hybridizes particle swarm optimization (PSO) and genetic algorithms (GA) with adaptive neuro-fuzzy inference system (ANFIS) models, assessing input parameters’ influence on biomethane yield through renowned performance metrics. Comparing the best model in the hybrid analysis, encompassing pretreatments A-E, the PSO-ANFIS (RMSE = 1.1719, MADE = 0.6525, MAE = 0.9314, Theil’s U = 0.1844, and SD = 0.7737) outperformed the GA-ANFIS (RMSE = 1.9338, MADE = 0.9318, MAE = 1.6557, Theil’s U = 0.2734, SD = 1.0598), using the same cluster radius of 0.50. Furthermore, compared to the GA-ANFIS model, the PSO-ANFIS model demonstrated significant improvements across various metrics: RMSE by 39.40%, MADE by 29.97%, MAE by 43.75%, Theil’s U by 32.56%, and SD by 27.00%. Results indicate that the PSO-ANFIS model outperforms the GA-ANFIS model, emphasizing the importance of suitable clustering algorithms and precise parameter adjustment for optimal performance in predicting biomethane yield from pretreated lignocellulose feedstocks. Graphical Abstract

Cultural adaptation and validation of the knee injury and osteoarthritis outcome score into Sinhala language in patients with primary knee osteoarthritis: a cross-sectional study

BackgroundKnee Injury and Osteoarthritis Outcome Score (KOOS) scale is a patient-reported outcome measurement tool. It evaluates both short- and long-term consequences of knee injury and primary osteoarthritis. This study aims to translate and validate the KOOS scale for a Sinhala-speaking Sri Lankan population.MethodsA cross sectional study was conducted in three hospitals. Four hundred and fifteen patients comprising 185 males and 227 females (3 subjects did not reveal their gender) with knee osteoarthritis (KOA) participated in the study. Seventy nine participants without KOA were recruited as controls. The functionality and quality of life level in patients and healthy participants were assessed using translated versions of the KOOS and Short Form-36 (SF-36) scales. Internal consistency of the instrument was assessed by Cronbach alpha. Construct validity and test-retest reliability were examined using the Intraclass Correlation Coefficient (ICC). Confirmatory Factor Analysis (CFA) was used to assess factorial validity.ResultsThe mean age (± sd) of the KOA subjects was 54.9 (± 9.2) years and for the control group was 49.2 (± 8.0) years. Majority of the respondents were female and Sinhalese in both groups. Internal consistency reliability was high (Cronbach’s alpha values ≥ 0.70). The test–retest reliability was excellent with the intraclass correlation coefficient for all subscales being above 0.90. Construct validity was assessed by the magnitude of the correlation coefficient between KOOS and SF-36 subscale scores. KOOS Pain scale moderately correlated with SF-36 bodily pain (Pearson’s r = 0.41). SF-36 physical function scores had a weak positive correlation with all KOOS subscales and SF-36 emotional wellbeing was not significantly correlated with KOOS Quality of Life (QoL) subscale. A five-factor Confirmatory Factor Analysis (CFA) model yielded a Comparative Fit Index (CFI) = 0.950, Tucker Lewis Index (TLI) = 0.946, Root Mean Square Error of Approximation (RMSEA) = 0.082 and Standardised Root Mean squared Residual (SRMR) = 0.072.ConclusionThe Sinhala translation of the KOOS scale is a reliable and valid instrument to assess KOA in a Sinhala-speaking Sri Lankan population. Studies to assess its use as a scale to evaluate responsiveness are recommended.

Dynamic response and blast resistance of I‐shaped steel‐concrete composite beam under explosive loading

AbstractTo investigate the dynamic mechanical response and damage mechanisms of I‐shaped steel‐concrete composite beams under explosive loads, experimental research and numerical simulations were conducted on steel‐concrete composite structures. The accuracy of the numerical analysis model was validated by comparing the damage characteristics of the structures obtained from explosive tests on the steel‐concrete composite components. Based on this, numerical simulations were performed on steel‐concrete composite beams using the explicit dynamic analysis software ANSYS/LS‐DYNA, and a study on their damage mechanisms was conducted. This study resulted in the acquisition of dynamic mechanical response patterns, including time‐dependent stress, strain, displacement, acceleration, and so on. Different structural damage characteristics under various explosive conditions were summarized, and key parameters affecting the blast resistance of the structure were analyzed. The research findings indicated that, in contrast to the failure characteristics of I‐shaped reinforced concrete (RC) beams, the failure characteristics of I‐shaped steel‐concrete composite beams mainly include punching and shearing failure of the steel‐RC slab and local buckling of the steel beam. Under the same explosive conditions, steel‐concrete composite structures exhibit superior blast resistance, with certain changes in structural parameters significantly improving blast resistance. The research results can provide theoretical support and a scientific basis for the proactive design of blast protection in steel‐concrete beams.

Predicting fatigue failure in five‐axis machined ball‐end milled components through FKM local stress approach

AbstractComponents created with five‐axis machining show a multi‐scale surface character due to cusps created on the surface and feed and tool marks within the cusps. Therefore, it becomes difficult to incorporate the effects of surface character on fatigue life for such components. In this work, an Forschungskuratorium Maschinenbau (FKM) guideline is adapted to develop a fatigue prediction model which considers cusps as notches and marks within the cusps as surface roughness (characterized by parameter R10z). The assessment uses stresses obtained from an finite element analysis model to predict the fatigue life of components whilst considering stress concentration, stress gradient, mean stress, and surface roughness effects. When cusps are regarded as surface roughness within the conventional FKM approach, fatigue life is considerably underestimated. In comparison, fatigue life predictions that take into consideration the roughness within cusps and treat cusps as stress‐raising notches are closer to experimental life.

A Prediction Method for the Average Winding Temperature of a Transformer Based on the Fully Connected Neural Network

The average winding temperature of a transformer (AWTT), serving as a key indicator for assessing the running state of the transformer, is of utmost importance in determining a transformer’s electrical properties and the insulation longevity of the transformer. An accurate prediction of AWTT is essential for ensuring the safe operation of the transformer. A novel method for predicting AWTT is introduced based on the analysis of field monitoring data. Firstly, the thermal characteristics and operational mechanisms of oil-immersed transformers are examined. Secondly, a factor analysis model is developed to streamline the network structure, accounting for the strong correlations among ambient temperature, load current, and top oil temperature. Thirdly, the independent temperature factor and load factor are extracted as pivotal features, and then input into the fully connected neural network to predict AWTT. Through a case study involving a 110 kV/10 kV oil-immersed transformer, the results show that the proposed method reduces redundant correlation information compared to traditional methods and improves the prediction accuracy of AWTT, establishing a foundation for further transformer state assessments.

Multi-physics field coupling interface lubrication contact analysis for gear transmission under various finishing processes

Surface integrity is crucial for the load-bearing capacity, transmission performance, and service life of gears. Finishing processes can effectively improve surface quality and are widely used in gear manufacturing processes to enhance surface integrity and improve the service performance of gears. Five precision machining tests of form grinding, vibratory finishing, barrel finishing, honing, and abrasive flow machining were conducted on spur gears. High-precision surface integrity characterization methods were used to analyze the surface integrity parameters of the finished gear surfaces, including surface roughness, surface microtopography, residual stress, texture aspect ratio, roughness peak curvature radius. The finishing processes uniformity and effectiveness of the tooth profile surface were also evaluated. By using the finished rough surfaces as inputs and considering the impact of thermal elasto hydrodynamic lubrication on the gear surface, a multi-field coupling contact analysis model encompassing solid, liquid, and thermal fields at the gear meshing interface was established. Lubrication performance parameters such as oil film pressure, oil film thickness, oil film temperature rise, and friction coefficient under various finishing surfaces were compared and analyzed. Experimental results indicate that barrel finishing can simultaneously improve various parameters, such as surface roughness, texture aspect ratio, and compressive residual stress. Finishing processes can effectively reduce surface roughness, improve lubrication performance and reduce the risk of scuffing failure. This approach provided an effective method for the comprehensive evaluation of the effects of various finishing processes. The developed program offers theoretical guidance and data support for the selection and optimization design of gear surface finishing processes.

Exploring Weak Magnetic Signal Characteristics of Pipeline Welds: Insights into Stress Non-Uniformity Effects.

Weak magnetic detection technology can detect stress concentration areas in ferromagnetic materials. However, the stress non-uniform characteristics of pipeline welds lead to significant differences in stress distribution range and values between inner wall welds and outer wall welds. This discrepancy makes it crucial to further evaluate the impact of stress non-uniformity on magnetic signals. To study the magnetic signal characteristics under the influence of residual stress in weld seams, a magneto-mechanical analytical model was established based on the magnetic charge theory and the distribution characteristics of residual stress in the weld seam. The magneto-mechanical relationship and magnetic signal distribution characteristics at the inner and outer wall welds of the pipeline are calculated. Furthermore, the effects of different excitation intensities on the amplitude growth characteristics of magnetic signals are analyzed and compared. To verify the analysis model, weld detection experiments with different excitation intensities were designed. The results show that both the peak-to-valley values of the normal component and the peak values of the tangential component of the outer wall weld are lower than those of the inner wall weld. Conversely, the peak-to-valley width of the normal component and the peak width of the tangential component are greater than those of the inner wall weld. Additionally, the rate of increase in weak magnetic signal amplitude decreases in a first-order exponential relationship with increasing excitation intensity. The average decay rates of the normal and tangential component amplitude growth rates for the inner wall weld are 34.03% and 27.9%, respectively, while for the outer wall weld, they are 31.75% and 28.01%, respectively. This study contributes to the identification and quantitative assessment of weak magnetic signals in inner and outer wall welds.

Applying (ARFIMA) Model for Forecast the Saudi Stock Market Prices

The interest in the topic of time series forecasting has increased during the recent years and thus appeared specific modern methods, for example Autoregressive Fractional Integrated Moving Average model (ARFIMA), or what is called long memory model, which use fractionally difference (d) instead of integer which used in ARIMA models. In this study we displayed long memory feature and its tests. Our discussion supported by analysing the real time series (daily closing index of the Saudi Arabia Stock Market prices) over period 1/1/2018 to 19/12/2022, including 1240 observations, to make a good model to giving forecast results for future, using statistical tests and statistical software (R- 4.2.2 program). Firstly we checked that the data series was unstable by testing unit root, using Dickey fuller and Philips Perron tests ; and confirmed the presence of a long memory pattern in it by calculating the Hurst exponent (H), then calculated the fractional differential coefficient (d), determined the appropriate models for analysis and prediction, then the best model was chosen among them based on the comparison criteria, then predicted the values for the next 5 days, R - 4.2.2 software was used in all of these tests and predictions Statistical results indicated that the optimal model to represent the data series is ARFIMA (3,0.383,2) which used to predict future values.

Adaptive Evolutionary Computing Ensemble Learning Model for Sentiment Analysis

Standard machine learning and deep learning architectures have been widely used in the field of sentiment analysis, but their performance is unsatisfactory if the input texts are short (e.g., social media posts). Specifically, the accuracy of standard machine learning methods crucially depends on the richness and completeness of the features used to represent the texts, and in the case of short messages, it is often difficult to obtain high-quality features. Conversely, methods based on deep learning can achieve better expressiveness, but these methods are computationally demanding and often suffer from over-fitting. This paper proposes a new adaptive evolutionary computational integrated learning model (AdaECELM) to overcome the problems encountered by traditional machine learning and deep learning models in sentiment analysis for short texts. AdaECELM consists of three phases: feature selection, sub classifier training, and global integration learning. First, a grid search is used for feature extraction and selection of term frequency-inverse document frequency (TF-IDF). Second, cuckoo search (CS) is introduced to optimize the combined hyperparameters in the sub-classifier support vector machine (SVM). Finally, the training set is divided into different feature subsets for sub-classifier training, and then the trained sub-classifiers are integrated and learned using the AdaBoost integrated soft voting method. Extensive experiments were conducted on six real polar sentiment analysis data sets. The results show that the AdaECELM model outperforms the traditional ML comparison methods according to evaluation metrics such as accuracy, precision, recall, and F1-score in all cases, and we report an improvement in accuracy exceeding 4.5%, the second-best competitor.