Effectiveness Of Model Research Articles

Hyperparameter optimization of YOLOv8 for smoke and wildfire detection: Implications for agricultural and environmental safety

In this study, we extensively evaluated the viability of the state-of-the-art YOLOv8 architecture for object detection tasks, specifically tailored for smoke and wildfire identification with a focus on agricultural and environmental safety. All available versions of YOLOv8 were initially fine-tuned on a domain-specific dataset that included a variety of scenarios, crucial for comprehensive agricultural monitoring. The ‘large’ version (YOLOv8l) was selected for further hyperparameter tuning based on its performance metrics. This model underwent a detailed hyperparameter optimization using the One Factor At a Time (OFAT) methodology, concentrating on key parameters such as learning rate, batch size, weight decay, epochs, and optimizer. Insights from the OFAT study were used to define search spaces for a subsequent Random Search (RS). The final model derived from RS demonstrated significant improvements over the initial fine-tuned model, increasing overall precision by 1.39 %, recall by 1.48 %, F1-score by 1.44 %, mAP@0.50 by 0.70 %, and mAP@0.50:0.95 by 5.09 %. We validated the enhanced model's efficacy on a diverse set of real-world images, reflecting various agricultural settings, to confirm its robustness in detecting smoke and fire. These results underscore the model's reliability and effectiveness in scenarios critical to agricultural safety and environmental monitoring. This work, representing a significant advancement in the field of fire and smoke detection through machine learning, lays a strong foundation for future research and solutions aimed at safeguarding agricultural areas and natural environments.

A deep learning approach for acute liver failure prediction with combined fully connected and convolutional neural networks.

Acute Liver Failure (ALF) is a critical medical condition with rapid development, often caused by viral infections, hepatotoxic drug abuse, or other severe liver diseases. Timely and accurate prediction of ALF occurrence is clinically crucial. However, predicting ALF poses challenges due to the diverse physiological differences among patients and the dynamic nature of the disease. This study introduces a deep learning approach that combines fully connected and convolutional neural networks for effective ALF prediction. The goal is to overcome limitations of traditional machine learning methods and enhance predictive model performance and generalization. The proposed model integrates a fully connected neural network for handling basic patient features and a convolutional neural network dedicated to capturing temporal patterns in patient data. The combination allows automatic learning of complex patterns and abstract features present in highly dynamic medical data associated with ALF. The model's effectiveness is demonstrated through comprehensive experiments and performance evaluations. It outperforms traditional machine learning methods, achieving 94.8% accuracy and superior generalization capabilities. The study highlights the potential of deep learning in ALF prediction, emphasizing the importance of considering individualized medical factors. Future research should focus on improving model robustness, addressing imbalanced data, and further exploring personalized features for enhanced predictive accuracy in real-world clinical scenarios.

Classification of gram-positive and gram-negative bacteria using Few-shot learning algorithm

Our research introduces a novel approach to classify bacteria as Gram-positive or Gram-negative using few-shot learning. We employ deep neural networks, specifically Prototypical Networks, to learn distinctive features from bacterial images, enabling accurate classification even with limited data. Experimental results on diverse datasets demonstrate the model's effectiveness and potential for real-world applications in microbiology and healthcare. We also address interpretability, ethics, and data privacy, making it a valuable tool for bacterial classification and diagnostics.

Improving Laryngoscopy Image Analysis Through Integration of Global Information and Local Features in VoFoCD Dataset.

The diagnosis and treatment of vocal fold disorders heavily rely on the use of laryngoscopy. A comprehensive vocal fold diagnosis requires accurate identification of crucial anatomical structures and potential lesions during laryngoscopy observation. However, existing approaches have yet to explore the joint optimization of the decision-making process, including object detection and image classification tasks simultaneously. In this study, we provide a new dataset, VoFoCD, with 1724 laryngology images designed explicitly for object detection and image classification in laryngoscopy images. Images in the VoFoCD dataset are categorized into four classes and comprise six glottic object types. Moreover, we propose a novel Multitask Efficient trAnsformer network for Laryngoscopy (MEAL) to classify vocal fold images and detect glottic landmarks and lesions. To further facilitate interpretability for clinicians, MEAL provides attention maps to visualize important learned regions for explainable artificial intelligence results toward supporting clinical decision-making. We also analyze our model's effectiveness in simulated clinical scenarios where shaking of the laryngoscopy process occurs. The proposed model demonstrates outstanding performance on our VoFoCD dataset. The accuracy for image classification and mean average precision at an intersection over a union threshold of 0.5 (mAP50) for object detection are 0.951 and 0.874, respectively. Our MEAL method integrates global knowledge, encompassing general laryngoscopy image classification, into local features, which refer to distinct anatomical regions of the vocal fold, particularly abnormal regions, including benign and malignant lesions. Our contribution can effectively aid laryngologists in identifying benign or malignant lesions of vocal folds and classifying images in the laryngeal endoscopy process visually.

Synergistic approach to tribological characterization of hybrid aluminum metal matrix composites with ZrB2 and fly ash: Experimental and predictive insights

This research delves into the tribological performance of hybrid aluminum metal matrix composites (HAMMCs) incorporating zirconium diboride (ZrB2) particles and fly ash as reinforcing agents. The study employs a linear reciprocating wear test to investigate the impact of dry sliding wear on these HAMMCs under ambient and elevated temperatures. Wear mechanisms are discerned through field emission scanning electron microscopy. Optimization of wear test parameters, coefficient of friction (COF), and wear rate is achieved using the genetic algorithm. Additionally, artificial neural network (ANN) and multiple linear regression analysis are employed to formulate a predictive model for wear, estimating specific wear rate and COF under various testing conditions. The ANN predictions exhibit a deviation ranging from 0% to 1.39% from the experimental values, indicating the model's effectiveness in understanding and predicting wear behavior in the study of HAMMC.

Dynamic economic dispatch of integrated energy system based on generative adversarial imitation learning

Within the context of energy structure transformation, the inherent uncertainty of new energy sources presents severe challenges to the optimized operation of integrated energy system. Model-driven optimization methods are limited by the accuracy and complexity of models, resulting in lower solving efficiency. Meanwhile, deep reinforcement learning methods that have gained attention in recent years face difficulties in designing rewards and slow convergence due to dealing with high-dimensional and complex dynamic information of integrated energy system. Therefore, inspired by imitation learning, this paper proposes a real-time dispatching method for integrated energy system based on generative adversarial imitation learning. Firstly, a generator network model is designed within the deep reinforcement learning framework, and expert prior knowledge is introduced to assist the generator network in iterative learning, enhancing the model's convergence effect. Secondly, drawing on the generative adversarial concept, a discriminator network model is developed to recognize expert and generated strategies, further designing game rewards to assist in updating the generator network parameters and avoiding the impact of subjectively defined reward functions on dispatching decisions. Finally, simulation analysis demonstrates that the proposed method can calculate the operation optimization solutions for integrated energy system more efficiently, significantly improving the accuracy of dispatching decisions and the model's convergence efficiency.

Screening and Identification of Neutrophil Extracellular Trap-related Diagnostic Biomarkers for Pediatric Sepsis by Machine Learning.

Neutrophil extracellular trap (NET) is released by neutrophils to trap invading pathogens and can lead to dysregulation of immune responses and disease pathogenesis. However, systematic evaluation of NET-related genes (NETRGs) for the diagnosis of pediatric sepsis is still lacking. Three datasets were taken from the Gene Expression Omnibus (GEO) database: GSE13904, GSE26378, and GSE26440. After NETRGs and differentially expressed genes (DEGs) were identified in the GSE26378 dataset, crucial genes were identified by using LASSO regression analysis and random forest analysis on the genes that overlapped in both DEGs and NETRGs. These crucial genes were then employed to build a diagnostic model. The diagnostic model's effectiveness in identifying pediatric sepsis across the three datasets was confirmed through receiver operating characteristic curve (ROC) analysis. In addition, clinical pediatric sepsis samples were collected to measure the expression levels of important genes and evaluate the diagnostic model's performance using qRT-PCR in identifying pediatric sepsis in actual clinical samples. Next, using the CIBERSORT database, the relationship between invading immune cells and diagnostic markers was investigated in more detail. Lastly, to evaluate NET formation, we measured myeloperoxidase (MPO)-DNA complex levels using ELISA. A group of five important genes (MME, BST1, S100A12, FCAR, and ALPL) were found among the 13 DEGs associated with NET formation and used to create a diagnostic model for pediatric sepsis. Across all three cohorts, the sepsis group had consistently elevated expression levels of these five critical genes as compared to the normal group. Area under the curve (AUC) values of 1, 0.932, and 0.966 indicate that the diagnostic model performed exceptionally well in terms of diagnosis. Notably, when applied to the clinical samples, the diagnostic model also showed good diagnostic capacity with an AUC of 0.898, outperforming the effectiveness of traditional inflammatory markers such as PCT, CRP, WBC, and NEU%. Lastly, we discovered that children with high ratings for sepsis also had higher MPO-DNA complex levels. In conclusion, the creation and verification of a five-NETRGs diagnostic model for pediatric sepsis performs better than established markers of inflammation.

Safeguarding the Internet of Things: Elevating IoT routing security through trust management excellence

This study presents an innovative IoT routing security model that integrates trust management to bolster network reliability, improve resilience against routing attacks, and isolate malicious activities. The model, emphasizing node behavior, reputation, and past performance, offers a nuanced approach to network security. Through comprehensive comparisons between dynamic and static models in IoT routing, the impact on crucial performance parameters, including throughput, packet delivery ratio, control traffic overhead, and energy consumption, is quantified. Simulations showcase the model's effectiveness in securing IoT communication, achieving an impressive 98 % accuracy in detecting and mitigating attacks. Comparative analysis against prior studies underscores its exceptional performance, particularly in identifying and classifying attack types such as wormhole, Sybil, and rank, alongside normal traffic. This trust-based IoT routing security model represents a substantial advancement in securing dynamic IoT environments, standing out as a valuable contribution. Noteworthy is its low average power consumption, contributing to its exceptional lightweight nature.

Construction and application of economic resilience evaluation model for megacities.

Economic resilience provides a new perspective for megacities to achieve sustainable development when facing multiple shocks, and its accurate evaluation is an essential prerequisite for optimizing urban governance. There are currently no generally accepted methods for empirical evaluation or measuring economic resilience, and the present study aims to contribute to in both the research field and methodology. The present study sets dimensions and indicators based on economic resilience's theoretical and empirical research and used Decision Making Trial and Evaluation Laboratory (DEMATEL) and Interactive Structural Modeling (ISM) methods to exclude the effect indicators and divide the indicator hierarchy, respectively. Subsequently, the present study conducts model validation using Chinese megacities as a case study. The game theory weighting method, which combines the Analytic Hierarchy Process (AHP) and Entropy methods, is used to calculate indicator weights, and the VIKOR (VIseKriterijumska Optimizacija i KOmpromisno Resenje) method is used to evaluate and compare economic resilience of megacities. The research findings indicate that the evaluation model constructed in the present study included 15 indicators (after excluding three effect indicators) divided into four levels. After merging the levels, they correspond to three dimensions: resistance, recoverability, and adaptability. In addition, using Chinese megacities as a case study, the evaluation results found that Beijing, Shanghai, and Shenzhen have high economic resilience, Tianjin and Guangzhou have moderate economic resilience, Chengdu has low economic resilience, and Chongqing has the lowest economic resilience. This result is consistent with previous studies and verifies the model's effectiveness. The present study also found that megacities with lower levels of economic resilience exhibit a more significant upward trend, as well as the highest and higher proportion of economic resilience in Chinese megacities depending on time passes, indicating that megacities' economic resilience is weakening. The evaluation result obtained in the present study is more specific, precise, and focused on depicting the distribution differences and development trends of economic resilience at the urban level.

A real-time surface defects detection model via dual-branch feature extraction and dynamic multi-scale fusion attention

Surface defect detection is a challenging task in industrial manufacturing, and the detection method based on deep learning has become the mainstream trend in the industry. However, the task of detecting defects on transparent plastic bottles poses unique challenges due to their clear, smooth surfaces and minimal texture, which contribute to specular reflections and make small, low-contrast defects difficult to discern. To tackle these challenges, we introduce the BiD-YOLO framework, an advanced version of the YOLOv5 model, tailored for real-time surface defect detection on such substrates. This framework notably enhances the detection accuracy for small, low-contrast defects and minimizes miss rates. At its core, BiD-YOLO incorporates a dual-branch feature extractor (DBFE) that utilizes both standard, dilated convolutions and a specialized small-object feature extraction branch to capture a broad spectrum of contextual information, optimizing the detection of fine details. Additionally, the dynamic multi-scale pyramid fusion attention (DyMFA) block with coordinate attention (CA) effectively addresses the loss of information in small-scale defects—a typical shortfall in conventional attention mechanisms—thereby improving detection precision across both channel and spatial dimensions. To achieve an optimal balance between accuracy and computational efficiency, we introduce two versions of the dynamic multi-scale pyramid attention block: multi-scale pyramid channel attention and dynamic channel attention. We also curated and annotated the TPSD dataset, a specialized collection of images showcasing surface defects on transparent plastic bottles under diverse real-world production conditions. Together with the NEU-DET dataset, these resources were employed to validate the BiD-YOLO model's effectiveness. Experimental results demonstrate a substantial improvement in defect detection capabilities, achieving mean average precision (mAP) values of 63.1% and 85.6%, respectively. Moreover, the two working models exhibit FPS values of 47.23FPS and 50.21FPS respectively. These findings underscore the BiD-YOLO model's pivotal role in reducing oversight in defect detection, thereby enhancing quality control within industrial manufacturing environments. The BiD-YOLO framework represents a significant advancement in the automated detection of surface defects on transparent plastic bottles, merging innovative technical features with robust practical applicability for industrial use.

PSO-PSP-Net + InceptionV3: An optimized hyper-parameter tuned Computer-Aided Diagnostic model for liver tumor detection using CT scan slices

An automated diagnostic system leads to a supreme requirement in medical image analysis, greatly impacting the death rate due to the high spreading rate of liver tumors. However, the traditional histopathological diagnostic processes cause more disruption in the medical detection framework because of the procedure of manual estimation of CT scans and medical labor-intensive, which lead to the time consumption process. It is a highly challenging task for the segmentation of liver tumor because of the irregular appearance, distinct densities, patchy edges, and tumor sizes. To overcome this issue, an automated computer-aided diagnosis framework has been utilized to diagnose the liver cancer quickly and accurately with the less workload of expert radiologists.Presently, Deep learning-based techniques have been applied to identify the tumor regions and assist radiologists in the initial diagnosis and scrutinizing the severity level. The proposed work presents a modified pyramid scene parsing network (PSP-Net) model based on InceptionV3 deep network architecture with a particle swarm optimization algorithm, which increases the efficiency and effectiveness of the liver diagnostic model. Three publicly accessible benchmark datasets have been engaged with the pre-processing task accomplished by image normalization and data augmentation techniques. Moreover, a hyper-parameter tuned improved PSO-PSP-Net algorithm has analyzed the optimal set of gathered features. In the experimental evaluation, the proposed model outperformed than grey wolf optimization (GWO-PSP-Net), PSO-U-Net, GWO-U-Net, opposition based spotted hyena optimization (O-SHO-CRNN), and grey wolf-class topper optimization (GW-CTO) + U-Net by 8.42 %, 4.9 %, 9.54 %, 5.12 %, and 1.75 %, respectively in terms of accuracy for LiTS dataset. The developed PSO-PSP-Net model attained better accuracy than GWO-PSP-Net, PSO-U-Net, GWO-U-Net, and Residual Atrous U-Net (RA-U-Net) for the segmentation of Type-I and Type-II liver tumors by 6.88 %, 8.44 %, 13.75 %, 5.99 % and 6.12 %, 9.74 %, 17.3 %, and 5.11 %, respectively for 3DIRCAD-b1 dataset. The obtained accuracy of the developed model is 14.09 %, 7.54 %, 16.36 %, 2.64 %, and 14.9 %, 8.86 %, 17.76 %, and 4.87 % maximized than GWO-PSP-Net, PSO-U-Net, GWO-U-Net, and RA-U-Net, respectively, for the segmentation of the infected arteries and bones in the 3DIRCAD-b1 dataset. For the CHAOS dataset, the improved PSO-PSP-Net model outperformed GWO-PSP-Net, PSO-U-Net, and GWO-U-Net, respectively, in terms of overall accuracy by 7.44 %, 8.85 %, and 17.2 %, respectively. The investigational analysis validates the proposed PSO-PSP-Net model's effectiveness and performance in obtaining high segmentation accuracy in comparison with other state-of-the-art approaches.

A novel support vector machine-based 1-day, single-dose prediction model of genotoxic hepatocarcinogenicity in rats.

The development of a rapid and accurate model for determining the genotoxicity and carcinogenicity of chemicals is crucial for effective cancer risk assessment. This study aims to develop a 1-day, single-dose model for identifying genotoxic hepatocarcinogens (GHCs) in rats. Microarray gene expression data from the livers of rats administered a single dose of 58 compounds, including 5 GHCs, was obtained from the Open TG-GATEs database and used for the identification of marker genes and the construction of a predictive classifier to identify GHCs in rats. We identified 10 gene markers commonly responsive to all 5 GHCs and used them to construct a support vector machine-based predictive classifier. In the silico validation using the expression data of the Open TG-GATEs database indicates that this classifier distinguishes GHCs from other compounds with high accuracy. To further assess the model's effectiveness and reliability, we conducted multi-institutional 1-day single oral administration studies on rats. These studies examined 64 compounds, including 23 GHCs, with gene expression data of the marker genes obtained via quantitative PCR 24h after a single oral administration. Our results demonstrate that qPCR analysis is an effective alternative to microarray analysis. The GHC predictive model showed high accuracy and reliability, achieving a sensitivity of 91% (21/23) and a specificity of 93% (38/41) across multiple validation studies in three institutions. In conclusion, the present 1-day single oral administration model proves to be a reliable and highly sensitive tool for identifying GHCs and is anticipated to be a valuable tool in identifying and screening potential GHCs.

Innovative approach to daily carbon dioxide emission forecast based on ensemble of quantile regression and attention BILSTM

Given the increasingly severe challenges of global climate change and sustainable development, accurate prediction of daily carbon dioxide (CO2) emissions has become crucial. However, current research on real-time daily forecasts remains relatively scarce, often limited to annual predictions and point estimates. This paper proposes an innovative integrated model, based on quantile regression and Attention-Bidirectional Long Short-Term Memory (BILSTM), specifically designed for probabilistic prediction of daily carbon emissions. Testing the model on daily carbon emission data from major carbon-emitting countries including China, the United States, India, Russia, and Italy has demonstrated its superior performance across various metrics, including mean squared error (MSE), mean absolute error (MAE), coefficient of determination (R2), and normalized average width of prediction intervals (PINAW), compared to other benchmark models. This achievement not only highlights the model's effectiveness in predicting daily carbon emissions but also provides policymakers with a powerful tool for real-time monitoring and assessment of carbon emissions. Furthermore, the application of probabilistic forecasting methods enables decision-makers to gain a more comprehensive understanding of the potential range of future carbon emissions, providing strong support for the formulation of targeted policies.

Detection of Human in Flame

This report explores the development of a real-time human-in-flames detection system using a YOLOv8 deep learning model. A carefully curated dataset of video frames depicting human-in-flames scenarios with corresponding bounding boxes was utilized to train the model. We explored various hyperparameter configurations and training techniques to optimize its performance. The model's effectiveness was evaluated on a separate validation dataset not used for training. Metrics like accuracy, precision, recall, and F1-score were employed to analyze the model's ability to generalize to unseen scenarios and identify people engulfed in flames within video streams. The findings of this project demonstrate the potential of YOLOv8 for human-in-flames detection, potentially contributing to improved fire safety systems and quicker response times in emergency situations.

Next‐generation energy‐efficient optical networking: DQ‐RGK algorithm for dynamic quality of service and adaptive resource allocation

SummaryIn green optical networking, designing an adaptive energy‐saving scheme plays a vital role, in optimizing energy consumption by dynamically adjusting resources based on network traffic and environmental conditions, to a more sustainable and efficient optical communication infrastructure. Traditional methods in optical networking face challenges such as static resource allocation, limited adaptability, inefficient power usage, environmental insensitivity, and scalability issues. Therefore this article proposed a novel method named Dynamic Quality of Service based Random update Genghis Khan (DQ‐RGK) algorithm, the proposed model can tackle the abovementioned complexities. In this study, cluster head dynamic placement is utilized to optimize the network's performance by adapting the placement of cluster heads to the current topology, load distribution, and energy levels in the network nodes. Additionally, Dynamic Quality of Service (QoS) is employed to respond dynamically to changes in network conditions, adapting to varying traffic patterns and resource availability. In this work, the Genghis Khan Shark optimization with a random update strategy is implemented for hyperparameter optimization to enhance the performance of the DQ‐RGK method. The DQ‐RGK adjusts the parameters of QoS in real‐time, and this ensures that network resources based on the requirements changed and priorities of applications, which ultimately optimizes performance and enhances user experience. By dynamically assigning and reallocating resources based on the current demand the algorithm enhances overall network efficiency and reduces energy consumption. Then, this work analyzes the experimental results, where some evaluation measures estimate the DQ‐RGK method's performance. Routing efficiency, latency, scalability, spectral efficiency, Packet Delivery Ratio, throughput, network lifetime, energy consumption, jitter, and energy consumption are the measures employed by the DQ‐RGK model. In The results, other routing models that do not provide efficiency are utilized, a comparison of these other routing models is represented in results. The overall DQ‐RGK model's effectiveness is represented in the experimental results and its effectiveness is greater among other methods.

Applying a Motivational Instructional Design Model to Stroke Rehabilitation: A Feasibility Study on Occupational and Swallowing Therapies

ObjectiveTo investigate the feasibility of poststroke interventions using a motivational instructional design model with occupational therapy (OT) and swallowing therapy (ST) and the model's potential physical and mental health effects. DesignAn open-label, single-arm, feasibility study on the Attention, Relevance, Confidence, and Satisfaction model. SettingTwo convalescent rehabilitation wards. ParticipantsTwenty-five patients with stroke (N=25) (19 men; mean age, 62.4±11.9y; 61.9±36.8d from the first stroke) were recruited. InterventionsTwelve participants received a motivational approach based on the Attention, Relevance, Confidence, and Satisfaction model during OT (OT group), and 13 received it during ST (ST group). The intervention lasted 40-60 minutes daily, 5 days weekly, for 4 weeks. Main Outcome MeasuresThe primary outcomes included the dropout rate, an adverse event, and the participants’ acceptability of the intervention. Paretic arm function was assessed in the OT group; swallowing ability was assessed in the ST group; and activities of daily living, depressive symptoms, and apathy were assessed in both groups. ResultsNo participants dropped out of the intervention or experienced an adverse event. Twenty-one participants (84%) were satisfied with the intervention, and 19 (76%) hoped to continue receiving it. The OT group showed statistically significant improvements in paretic arm function and activities of daily living (Cohen's r=0.68-0.77), whereas the ST group improved in swallowing ability, activities of daily living, and depressive symptoms (Cohen's r=0.62-0.85). ConclusionsThe interventions using the motivational instructional model with OT and ST were feasible and could improve poststroke paretic arm function, swallowing ability, and activities of daily living after stroke.

Modeling and optimization of the ratio of fluorophores: a step towards enhancing the sensitivity of ratiometric probes.

In the ratiometric fluorescent (RF) strategy, the selection of fluorophores and their respective ratios helps to create visual quantitative detection of target analytes. This study presents a framework for optimizing ratiometric probes, employing both two-component and three-component RF designs. For this purpose, in a two-component ratiometric nanoprobe designed for detecting methyl parathion (MP), an organophosphate pesticide, yellow-emissive thioglycolic acid-capped CdTe quantum dots (Y-QDs) (analyte-responsive), and blue-emissive carbon dots (CDs) (internal reference) were utilized. Mathematical polynomial equations modeled the emission profiles of CDs and Y-QDs in the absence of MP, as well as the emission colors of Y-QDs in the presence of MP separately. In other two-/three-component examples, the detection of dopamine hydrochloride (DA) was investigated using an RF design based on blue-emissive carbon dots (B-CDs) (internal reference) and N-acetyl L-cysteine functionalized CdTe quantum dots with red/green emission colors (R-QDs/G-QDs) (analyte-responsive). The colors of binary/ternary mixtures in the absence and presence of MP/DA were predicted using fitted equations and additive color theory. Finally, the Euclidean distance method in the normalized CIE XYZ color space calculated the distance between predicted colors, with the maximum distance defining the real-optimal concentration of fluorophores. This strategy offers a more efficient and precise method for determining optimal probe concentrations compared to a trial-and-error approach. The model's effectiveness was confirmed through experimental validation, affirming its efficacy.

Building College Education Management in the New Era: Theoretical Discussion and Model Construction Based on New Quality Productivity

This study analyses the use of new quality productivity theory in higher education management and develops a framework called the "multi-dimensional collaborative innovation management model" to tackle the challenges of globalisation and information technology. This concept combines the three dimensions of scientific and technological innovation, management innovation, and system innovation to promote effective resource allocation, improve educational quality and efficiency, and increase the education system's adaptability. This study uses mixed-methods research to verify the model's effectiveness in practical educational management as well as to present fresh theoretical perspectives and practical tactics for cultivating high-qualified talents who will satisfy the needs of future society.

Empowering English Language Learners: Mitigating Speaking Anxiety through Team Assisted Individualization (TAI) Model in the EFL Junior Highschool Context

This study investigated the efficacy of the Team-Assisted Individualization (TAI) model in reducing speaking anxiety among ninth-grade students at SMPN 30 Makassar. The research utilized a pre-experimental design, collecting data through observations and Likert Scale questionnaires analysed descriptively. Initially, none of the 20 students experienced low levels of anxiety; however, post-treatment results showed a significant reduction in anxiety levels with no students reporting high or very high anxiety, and 80% reporting low anxiety. Significantly, the mean questionnaire score increased from 49.25 before the intervention to 82.10 afterwards, indicating a substantial decrease in anxiety. This outcome aligns with existing literature suggesting that lower anxiety levels facilitate better language performance. Observational data also reflected a decrease in student engagement from 89% in the first session to 57% in the fourth, suggesting varying levels of participation throughout the study. The TAI model's effectiveness in significantly lowering anxiety was confirmed through a t-test showing a significance level of 0.000, which is well below the conventional threshold of 0.05, thereby supporting the acceptance of the hypothesis that TAI reduces speaking anxiety. These findings underscore the potential of interactive and individualized teaching strategies in reducing language learning anxiety. The sustained engagement and active participation fostered by the TAI model suggest that such approaches can significantly enhance language learning environments by reducing anxiety and encouraging more active involvement from students.

A hybrid machine learning forecasting model for photovoltaic power

The increasing use of photovoltaic (PV) power generation presents a significant opportunity for global energy transformation. However, accurately forecasting PV power remains a challenge. This study proposes a hybrid approach that combines variational mode decomposition (VMD), whale optimization algorithm (WOA), and long short-term memory neural network (LSTM) to forecast photovoltaic (PV) power accurately. The model decomposes the time series of PV power using VMD to address the non-stationary nature of the time series. The VMD parameters are optimized using WOA. Subsequently, and the PV power time series data is then decomposed using the optimized VMD parameters to obtain multiple intrinsic mode functions (IMFs) components and a residual component. These components are then reconstructed with meteorological parameters to obtain the reconstructed IMF components and residuals. Finally, multiple LSTM sub-models are built, with each of them taking the IMF components and residual from the previous reconstruction as inputs. The sub-models are optimized using the WOA method to determine their hyperparameters and then constructed with these optimized hyperparameters. The predicted values of each IMF and residual are output and added for sequence reconstruction to derive the final predicted value of PV power. The model's effectiveness was verified for one-hour-ahead forecasting at the 1.8 MW solar system in Yulara, central Australia. The test results show that the proposed model outperforms other benchmark models, with mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared value (R2) of 15.247 kW, 19.753 kW, 4.405 % and 0.997, respectively. Compared to the LSTM, MAE, RMSE, and MAPE decreased by 84.942 %, 86.746 %, and 82.611 %, respectively, while R2 increased by 23.438 %. The proposed model has better predictive performance for both stable power changes and large fluctuations, essential for effectively integrating renewable energy sources into the power grid.