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Background: Gangrenous cholecystitis (GC) is a serious clinical condition associated with high morbidity and mortality rates. Machine learning (ML) has significant potential in addressing the diverse characteristics of real data. We aim to develop an explainable and cost-effective predictive model for GC utilizing ML and Shapley Additive explanation (SHAP) algorithm. Results: This study included a total of 1006 patients with 26 clinical features. Through 5-fold CV, the best performing integrated learning model, XGBoost, was identified. The model was interpreted using SHAP to derive the feature subsets WBC, NLR, D-dimer, Gallbladder width, Fibrinogen, Gallbladder wallness, Hypokalemia or hyponatremia, these subsets comprised the final diagnostic prediction model. Conclusions: The study developed a explainable predictive tool for GC at an early stage. This could assist doctors to make quick surgical intervention decisions and perform surgery on patients with GC as soon as possible. Key Summary: Using clinical data from 1006 cholecystitis patients, we developed a machine learning-based diagnostic prediction model to help identify patients at high risk for acute gangrenous cholecystitis. During the study, the deficiency and imbalance of actual clinical data were directly addressed, leading to the ultimate selection of the integrated learning model XGBoost as the predictive model exhibiting superior performance and stability on a novel, unidentified validation set and compared to preoperative clinical diagnosis. The model employs variables that are non-specific, readily available, reasonably priced, and appropriate for clinical generalization. [ABSTRACT FROM AUTHOR]

Background: The present study aimed to assess the efficacy of applying active learning and integrated methodologies in the undergraduate Pathology medical education curriculum. Another goal was to acquire student feedback and explore the students' motivation and perceptions on Pathology education. Methods: Our study involved two parts: (1) a quasi-experimental part, involving medical students enrolled in our Pathology course. An optional educational intervention was offered until the end of the semester, in parallel with the already established curriculum. The intervention involved integrated case-based seminars and group-based presentations of Pathology cases. We used a multivariate regression model in order to better assess the educational effectiveness of our method; and (2) a questionnaire-based cross-sectional survey was held at the end of the semester. The set of questions for the intervention group mainly involved feedback about our educational method, while the questions for the control group involved questions about Pathology education in general. Results: 348 Students completed the study. The performance of the intervention group was significantly improved at post-test. From the questionnaire, we found that intervention participants gave positive feedback, while their main reason for participation was because they already valued the Pathology professor's teaching style. As for non-participants, even though most of them were interested in an integrated/experiential approach, more than half chose not to participate because of lack of time in general or in order to study for the final exams. Conclusions: Active learning and integrated methodologies seem to be of value in undergraduate Pathology teaching. However, regardless of the instructional approach, shifting the emphasis from purely summative achievement to formative, learning-centered development is essential. A successful educator must therefore not only employ innovative methods but also encourage intrinsic motivation and a deeper, more reflective approach to learning. [ABSTRACT FROM AUTHOR]

This study explores a method to improve the site selection for elderly care facilities in an aging region, using Hefei City, China, as the study area. It combines topographic conditions, population distribution, economic development status, and other multi-source spatial big data at a 500 m grid scale; constructs a prediction model for the suitability of sites for elderly care facilities based on integrated learning; and carries out a comprehensive evaluation and feature importance analysis. Finally, it uses trained random forest (RF) and gradient boosting decision tree (GBDT) models to predict preliminary site selection results for elderly care facilities. A second screening that compares three degrees of population aging is conducted to obtain the final site selection results. The results show the following: (1) The comprehensive evaluation indexes of the two integrated learning models, RF and GBDT, are above or below 80% as needed, which is better than the four single learning models. (2) The prediction results of the RF and GBDT models have 87.9% and 78.4% fit to existing elderly facilities, respectively, which indicates that the methods are reasonable and reliable. (3) The results of both the RF and GBDT models indicate that the closest distance to healthcare facilities and the size of the population distribution are the two most important factors affecting the location of elderly care facilities. (4) The results of the preliminary site selection show an overall spatial distribution of higher suitability in the main urban area and lower suitability in the suburban counties. The secondary screening finds that priority needs to be given to the periphery of the main urban area and to Lujiang County and other surrounding townships that have a more serious degree of aging as soon as possible in the site selection of new elderly care facilities. [ABSTRACT FROM AUTHOR]

The soluble solid content (SSC) is a primary characteristic index for evaluating the internal quality of citrus fruits. The development of rapid and nondestructive SSC detection techniques can help address the current issues of postharvest quality grading in China's citrus industry. In this study, a total of 261 experimental samples, including 70 Murcott, 91 Clementine, and 100 Navel orange, were divided into prediction and validation sets in a 7:3 ratio. After obtaining the reflection spectra and SSCs, SNV‐FOD (Standard Normal Variate—Fractional‐Order Derivative) was used to process the spectra, and the optimal band combination algorithm was introduced to select SSC‐sensitive bands. Then, the obtained optimal dual‐band combination was input into eight regression models for comparison, and the best performing models stacked ensemble models was selected. Finally, the H‐ELR (HyperOpt‐optimized ensemble learning regression) model, optimized using a Bayesian function, was applied for the effective estimation of SSC for three common citrus varieties in Guangxi, Murcott, Clementine, and Navel oranges. The results show that (1) the SNV‐FOD preprocessing method proposed in this study improved the correlation coefficient with the SSC from 0.546 to 0.836 compared to that of the original spectrum, (2) the optimal dual‐band combination (969 and 1069 nm) constructed by integrating the differential index and 1.2‐order derivative yielded the most accurate results (RPD = 2.13), and (3) the H‐ELR model, based on HyperOpt optimization, achieved good estimated performance (RPD = 2.46). Practical Application: This research contributes to the development of practical SSC prediction instruments with excellent universality and ease of application. [ABSTRACT FROM AUTHOR]

DNA methylation is of crucial importance for biological genetic expression, such as biological cell differentiation and cellular tumours. The identification of DNA-6mA sites using traditional biological experimental methods requires more cumbersome steps and a large amount of time. The advent of neural network technology has facilitated the identification of 6 mA sites on cross-species DNA with enhanced efficacy. Nevertheless, the majority of contemporary neural network models for identifying 6 mA sites prioritize the design of the identification model, with comparatively limited research conducted on the statistically significant DNA sequence itself. Consequently, this paper will focus on the statistical strategy of DNA double-stranded features, utilising the multi-head self-attention mechanism in neural networks applied to DNA position probabilistic relationships. Furthermore, a new recognition model, PSATF-6 mA, will be constructed by continually adjusting the attentional tendency of feature fusion through an integrated learning framework. The experimental results, obtained through cross-validation with cross-species data, demonstrate that the PSATF-6 mA model outperforms the baseline model. The in-Matthews correlation coefficient (MCC) for the cross-species dataset of rice and m. musus genomes can reach a score of 0.982. The present model is expected to assist biologists in more accurately identifying 6 mA locus and in formulating new testable biological hypotheses. [ABSTRACT FROM AUTHOR]

The paper presents a scoping review of research that has been conducted on integrated STEM programmes in Australian schools over a 10-year period (2012–2022). It aims to determine how integrated STEM is being practiced. A total of 17 papers were chosen for review. The review explored the major characteristics of research that has been conducted on integrated STEM programmes in Australian schools, the teaching strategies used to teach integrated STEM and the reported impacts of these programmes. In fifteen of the papers experts from outside the school such as scientists and engineers, usually academics, were involved. Cooperative learning, project/problem-based learning and authentic experiences were the most common teaching strategies in a field characterised by positive learner outcomes. It is evident that teaching strategies are significant in the integration of STEM. Future research should focus on addressing issues of ecological validity, sustainability and scalability to ensure as many students as possible reap the benefits of high-quality STEM education. [ABSTRACT FROM AUTHOR]

The purpose of the present two studies is to design an integrated content of the third-grade elementary math textbook using a quantitative approach in the first study, and in the second study, to assess the effect on students' integrated learning. In this study, the qualitative part used the qualitative content analysis method and the quantitative part applied the quasi-experimental method of pre-test and post-test design with a control group. In the qualitative section, a sample of 18 expert teachers from Izeh was selected using the purposive sampling method. In the quantitative section, a sample of 30 people was selected by a simple random sampling method. The statistical population of the quantitative section is all primary school male students in Izeh City. In this study, first a conceptual model was designed, then using qualitative content analysis and the interview tool, the participants 'opinions on the possibility of linking the content of the math textbook with three third-grade elementary textbooks were extracted and then the effect of this content on students' learning was compared with three third grade textbooks. The results show a kind of unity of sciences with a significant difference between integrated learning of students who have been trained with integrated content and students who have not been trained with integrated content, and the level of significance is less than 0.05, indicating that integrated content is based on integrated learning, with integration playing a very important role. [ABSTRACT FROM AUTHOR]

Background. The relevance of the problem is due to the fact that the Federal State Educational Standards of Higher Education consolidate the line on a personality-oriented model of education and upbringing. This model is designed to help in finding and creating conditions for the development of universal, general professional and professional competencies in the studing of bachelors. Purpose. Creation of a course of the second foreign language (German) for bachelor students in politics based on interdisciplinary connections of philological subjects. For this purpose, the authors use the basics of an interdisciplinary approach, which allows students to apply knowledges of the disciplines of the curriculum, such as "Political Science", "Practice of Effective Communication", "Logic and Theory of Argumentation", "Cultural Studies", "Russian Language and Culture of Speech", "Geopolitics", "Fundamentals of Internet Communication" in the process of studying a foreign language and improving universal, general professional and professional competencies. As a result of applying an interdisciplinary approach, the scientific picture of the world of students is expanded, the analysis of foreign experience of public administration and international law is carried out in the original language. Profile-oriented approach is achieved through the implementation of a communicative, personal, activity-oriented approach in teaching related and close in content disciplines. Materials and methods. The study material was based on the texts for training from the textbook "45 Stunden Deutschland", the Internet portal "Tatsachen über Deutschland" and project assignments developed by students studying in the specialty "Public Policy and Social Sciences" in the 3rd and 4th years of the "Magnitogorsk State Technical University named after G.I. Nosov". We assume that when preparing bachelor's students in non-linguistic specialties, it is necessary to build the teaching of a foreign language based on the interdisciplinary connections of general humanitarian disciplines, then the level of foreign language communicative competence will remain no lower than the level of senior secondary school and in some cases will be higher, and the level of universal, general professional and professional competencies of a bachelor-future politician by means of a second foreign language (FL2) will increase. In our study, we analyzed the literature on the problem, summarized the existing developments of domestic and foreign scientists on the topic of the study, and conducted experimental training. We are confident that the application of the competence approach in assessing foreign language communicative competence in FL2 allows us to state that the level of proficiency in FL2 becomes higher, namely equal to level B1 or B1+. This, in turn, ensures the students' readiness for further language and speech development by means of both a foreign and native language. Results. The socio-political and economic changes taking place in our country have led to a qualitatively new round of interest in foreign languages and the subject of "Foreign language". The desire to improve the preparation of bachelors in foreign languages has disrupted the usual monotony of curricula and work programs. Magnitogorsk State Technical University named after G.I. Nosov, represented by the Institute of Economics and Management, conducts bachelor's degree training in the direction 41.03.06 "Public Policy and Social Sciences" and successfully implements programs that meet the requirements of today. Bachelors of this specialty are taught two foreign languages, one of which is German language. Experience working in groups of the specified specialty convinces that through interest in the language, interest in the country of the studied language also increases. The level of language proficiency allows students to critically analyze foreign sources in their specialty and make reasonable conclusions, perceive the intercultural diversity of society, apply the knowledge gained during language training when preparing reports, presentations, carry out business communication in a foreign language, be ready for intercultural dialogue and defend their civic position. That is, in general, students improve their communication skills in all communication functions: informational, regulatory, emotional-evaluative, etiquette.

To accurately locate electricity theft and reduce the economic losses caused by electricity theft to the power system, a multi-classification electricity theft detection model based on the entropy weight method Stacking E_Stacking(stacking based entropy) ensemble learning was proposed. First, based on the collinear characteristics of electricity consumption information, the VIF(variance inflation factor)was used as a standard to reduce the dimensionality of the data to reduce data complexity. Then k-fold cross-validation was embedded during model training to effectively prevent model overfitting. This model contained two layers of learners, a primary learner, and a meta-learner, it could fully combine the advantages of the two-layer learner and combine the learned complementary features and discriminative features to further improve detection performance. Finally, the Irish dataset and a portion of UCI(University of California Irvine)datasets were used to verify the model, and the results were better than several common methods at present, which demonstrates that the model has certain effectiveness and robustness. [ABSTRACT FROM AUTHOR]

In the middle southern section of the Freshwater River–Small River Fault system, Xichang City, Daliang Prefecture, Sichuan Province, is situated in the junction between the Anning River Fault and the Zemu River Fault. There has been a risk of increased activity in the fault zone in recent years, and landslide susceptibility evaluation for the area can effectively reduce the risk of disaster occurrence. Using integrated learning and Bayesian hyperparameter optimization, 265 landslides in Xichang City were used as samples in this study. Thirteen influencing factors were chosen to assess landslide susceptibility, and the BO-XGBoost, BO-LightGBM, and BO-RF models were evaluated using precision, recall, F1, accuracy, and AUC curves. The findings indicated that after removing the terrain relief evaluation factor, the four most significant factors associated with landslide susceptibility were NDVI, distance from faults, slope, and distance from rivers. The study demonstrates that the AUC value of the BO-XGBoost model in the study area is 0.8677, demonstrating a better generalization ability and higher prediction accuracy than the BO-LightGBM and BO-RF models. After Bayesian optimization of hyperparameters, the model offers a significant improvement in prediction accuracy. [ABSTRACT FROM AUTHOR]

Current machine learning models under artificial intelligence can only improve prediction accuracy, but their underlying logic remains incomprehensible. Therefore, to provide high prediction accuracy and enhance the interpretability of the model through machine learning, the study selects the Extreme Gradient Boosting (XGBoost) model by comparing multiple models under single learner and integrated learning. Then a cancer probability statistical prediction model is constructed through parameter optimization, and its performance and interpretability are analyzed. The experimental results showed that the Receiver Operating Characteristic (ROC) Area under Curve (AUC) value in the single learner was generally lower than 80%, while the AUC value was 84.4%, surpassing that of the comparison model. Simultaneously, an increase in Alpha-Fetoprotein value greater than 13.5 had a stronger predictive effect when combined with other factors. Smaller serum Alanine Aminotransferase and Alpha-Fetoprotein assay near 0 may produce negative or positive effects, whereas a higher value is more likely to produce a positive effect. This is in line with its clinical significance. Overall, XGBoost effectively improves the out-of-sample prediction accurate and interpretability, which is significant for the actual liver cancer diagnosis prediction. [ABSTRACT FROM AUTHOR]

Three-dimensional printing technology is a rapid prototyping technology that has been widely used in manufacturing. However, the printing parameters in the 3D printing process have an important impact on the printing effect, so these parameters need to be optimized to obtain the best printing effect. In order to further understand the impact of 3D printing parameters on the printing effect, make theoretical explanations from the dimensions of mathematical models, and clarify the rationality of certain important parameters in previous experience, the purpose of this study is to predict the impact of 3D printing parameters on the printing effect by using machine learning methods. Specifically, we used four machine learning algorithms: SVR (support vector regression): A regression method that uses the principle of structural risk minimization to find a hyperplane in a high-dimensional space that best fits the data, with the goal of minimizing the generalization error bound. Random forest: An ensemble learning method that constructs a multitude of decision trees and outputs the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. GBDT (gradient boosting decision tree): An iterative ensemble technique that combines multiple weak prediction models (decision trees) into a strong one by sequentially minimizing the loss function. Each subsequent tree is built to correct the errors of the previous tree. XGB (extreme gradient boosting): An optimized and efficient implementation of gradient boosting that incorporates various techniques to improve the performance of gradient boosting frameworks, such as regularization and sparsity-aware splitting algorithms. The influence of the print parameters on the results under the feature importance and SHAP (Shapley additive explanation) values is compared to determine which parameters have the greatest impact on the print effect. We also used feature importance and SHAP values to compare the importance impact of print parameters on results. In the experiment, we used a dataset with multiple parameters and divided it into a training set and a test set. Through Bayesian optimization and grid search, we determined the best hyperparameters for each algorithm and used the best model to make predictions for the test set. We compare the predictive performance of each model and confirm that the extrusion expansion ratio, elastic modulus, and elongation at break have the greatest influence on the printing effect, which is consistent with the experience. In future, we will continue to delve into methods for optimizing 3D printing parameters and explore how interpretive machine learning can be applied to the 3D printing process to achieve more efficient and reliable printing results. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the era of industrial revolution, education is required to produce graduates who are in line with 21st century skills. One of the learning models that is considered suitable for the challenges of this century is integrative learning. Specifically, this research explores the concept of interdisciplinary integrative learning in Indonesian language learning, starting from the principles, steps, to the impact of learning for students. This research used the SLR (systematic literature review) method. Data was obtained through the Publish or Perish application. From 224 articles found, 5 articles were selected that were in line with this research. Based on the search results, it was found that the principles of learning with the integrative learning model are (1) student-centered learning; (2) integrating various disciplines; and (3) utilizing technology. The implementation of integrative learning in Indonesian language learning can be done by combining various learning methods or various disciplines in learning. Through this model, Indonesian language learning at school becomes more meaningful because students are able to master linguistic insights and can use them in everyday life. In addition, this model also helps students to understand knowledge holistically to solve problems in everyday life.

DNA methylation is of crucial importance for biological genetic expression, such as biological cell differentiation and cellular tumours. The identification of DNA-6mA sites using traditional biological experimental methods requires more cumbersome steps and a large amount of time. The advent of neural network technology has facilitated the identification of 6 mA sites on cross-species DNA with enhanced efficacy. Nevertheless, the majority of contemporary neural network models for identifying 6 mA sites prioritize the design of the identification model, with comparatively limited research conducted on the statistically significant DNA sequence itself. Consequently, this paper will focus on the statistical strategy of DNA double-stranded features, utilising the multi-head self-attention mechanism in neural networks applied to DNA position probabilistic relationships. Furthermore, a new recognition model, PSATF-6 mA, will be constructed by continually adjusting the attentional tendency of feature fusion through an integrated learning framework. The experimental results, obtained through cross-validation with cross-species data, demonstrate that the PSATF-6 mA model outperforms the baseline model. The in-Matthews correlation coefficient (MCC) for the cross-species dataset of rice and m. musus genomes can reach a score of 0.982. The present model is expected to assist biologists in more accurately identifying 6 mA locus and in formulating new testable biological hypotheses.

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The research in the field of mobile assisted language learning has a long history. It basically follows the route from theory to application practice, but there are few process studies on learners' individual language skills learning behavior based on mobile platform data. This research takes vocabulary learning as the starting point, and constructs a mobile user English learning pattern recognition model with improved Stacking integration algorithm. The purpose of this study is to identify different learning modes by analyzing the learning behavior and learning data of mobile users, and to provide personalized learning suggestions for users. The evaluation goal of this study is the accuracy and robustness of the mobile users' English learning pattern recognition model, and the accuracy is the classification accuracy of the model for different learning patterns. Robustness is the stability and consistency of the model in different situations. In order to evaluate the integrated English learning mode of mobile users, the study first divides the collected learning data set into training set and test set. In this step, the method used in the study is cross-validation, which aims to reduce the difference of evaluation results caused by different data sets. For the relevant features in mobile users' learning data, the features extracted by the research include learning behavior and learning progress, which can accurately reflect the learning mode. Then, the integrated learning method is used to train the model, and the best parameter combination is selected through the training set. Finally, the study uses the test set to evaluate the trained model, and calculates the accuracy and recall index of the model on the test set. Through this evaluation method, the evaluation results of the integrated learning pattern recognition model in terms of accuracy and robustness are obtained, and reference is provided for the improvement of the model. The model proposed in this study is suitable for a large number of user data. Because the learning behavior of users is influenced by personal habits, there are limitations in obtaining enough high-quality data, so the labeling of data is subjective. It is still a challenge to select the most representative features of user learning behavior extracted from the model. The feature selection method can lead to different results, and the process requires a lot of human intervention. The experiment conducted mining and analysis on user learning behavior data of a domestic English vocabulary learning APP. Compared with the confusion matrix of the traditional Stacking model, the improved Stacking model has a stronger ability to distinguish user learning patterns. According to the formula, the accuracy of the improved Stacking model is 91.29%; The accuracy of traditional Stacking model is 90.71%. The ROC curve of the improved Stacking model is smoother than the three single models. Its AUC value is 0.85, which is the same as that of XGBoost. The function is also improved compared with the traditional Stacking model, Logistic Regression (LR) and Random Forest (RF) model. Therefore, the Stacking integrated model owns the best forecast performance and can accurately predict the long-term learning mode of users. In this study, the English learning patterns of mobile users are identified by the method of integrated learning, so that the prediction results of multiple basic learners can be integrated, and the complementarity between different learners can be effectively dealt with, thus improving the generalization ability of the model. This model aims at identifying the English learning patterns of mobile users, and can accurately identify the learning patterns of users by analyzing their learning behaviors on mobile devices. This is of great significance for personalized English learning recommendation. The English learning pattern recognition model for mobile users proposed in this study can identify users' learning patterns by analyzing their learning behaviors, thus providing personalized learning support and suggestions. In the process of digital manufacturing, they can learn from the idea of learning pattern recognition model and identify the patterns and laws in the production process by analyzing production data and process parameters, so as to optimize the production process and improve production efficiency. [ABSTRACT FROM AUTHOR]

Students' performance in higher education reflects their overall quality in higher education. By predicting the performance, students with greater learning problems can be screened out early and given appropriate guidance. To predict students' performance in English, the knowledge information of courses, examination papers, and historical examination records are used to build a feature project of students' examinations. Meanwhile, the features strongly correlated with their performance are filtered out. Then the next step of performance prediction is carried out. The results showed that a neural network long and short-term memory performance prediction model incorporating an attention mechanism was more effective than other models in predicting English performance in higher education. Further experiments found that the model reduced the error by 1.04% on the MAE metric, 0.53% on the RMSE metric, and increased its value by 4.12% on the R2 metric. Adding the new feature dataset led to better forecasting by the Att-LSTM model in all metrics. This indicated that the enhanced dataset temporality could improve the effectiveness of the Att-LSTM model in predicting English grades in higher education. The stacked integrated prediction model, by integrating multiple strong regressors, can avoid poor prediction and excessive overall bias due to one regressor and increase the soundness and prediction precision of the mode. [ABSTRACT FROM AUTHOR]

The intelligent architecture based on the microgrid (MG) system enhances distributed energy access through an effective line network. However, the increased paths between power sources and loads complicate the system's topology. This complexity leads to multidirectional line currents, heightening the risk of current loops, imbalances, and potential short-circuit faults. To address these challenges, this study proposes a new approach to accurately locate and identify faults based on MG lines. Initially, characteristic indices such as fault voltage, voltage fundamentals at each MG measurement point, and extracted features like peak voltage values in specific frequency bands, phase-to-phase voltage differences, and the sixth harmonic components are utilized as model inputs. Subsequently, these features are classified using the Lightweight Gradient Boosting Machine (LightGBM), complemented by the bagging (Bootstrap Aggregating) ensemble learning algorithm to consolidate multiple strong LightGBM classifiers in parallel. The output classification results of the integrated model are then fed into a neural network (NN) for further training and learning for fault-type identification and localization. In addition, a Shapley value analysis is introduced to quantify the contribution of each feature and visualize the fault diagnosis decision-making process. A comparative analysis with existing methodologies demonstrates that the LightGBM-NN model not only improves fault detection accuracy but also exhibits greater resilience against noise interference. The introduction of the bagging method, by training multiple base models on the initial classification subset of LightGBM and aggregating their prediction results, can reduce the model variance and prevent overfitting, thus improving the stability and accuracy of fault detection in the combined model and making the interpretation of the Shapley value more stable and reliable. The introduction of the Shapley value analysis helps to quantify the contribution of each feature to improve the transparency and understanding of the combined model's troubleshooting decision-making process, reduces the model's subsequent collection of data from different line operations, further optimizes the collection of line feature samples, and ensures the model's effectiveness and adaptability. [ABSTRACT FROM AUTHOR]

Addressing calls to develop assessment theories for integrated teaching and learning, we propose an evidence‐informed perspective on grading in Science, Technology, Engineering, Arts and Mathematics (STEAM) education. We leveraged a qualitative collective case study design to generate rich profiles of three exemplary STEAM teachers' grading approaches and practices. Data sources included semi‐structured interviews and artefacts of teachers' instruction and assessment practice. We analysed qualitative data from interviews and artefacts using a general inductive approach. The teachers in our study pushed back against 'objective' views of grading, whereby grades are composites of summative assessments, in favour of informed and contextualised grading, which aims to document and support a negotiated understanding of each student's learning journey. Teachers' grading practices aligned with a hermeneutic approach to classroom assessment validity: the teachers (a) collected and interpreted a wide range of evidence of student (re)learning; (b) centred students' perspectives and evidences; and (c) employed their professional judgement to determine students' grades. Teachers characterised grading as a process of accounting for all available evidence, blurring the boundaries between formative and summative assessment. Documenting the learning process, rather than focusing on products, can support deeply integrated learning. Importantly, the teachers supported students in documenting their own learning and negotiating their grades with reference to self‐generated evidence. This practice stands to reduce power imbalances between students and teachers and foster students' self‐regulated learning. Our findings inform a framework which STEAM educators can use to guide grading in integrated classrooms, an enduring challenge for integrated learning. [ABSTRACT FROM AUTHOR]

Starch and alcohol serve as pivotal indicators in assessing the quality of lees fermentation. In this paper, two hyperspectral imaging (HSI) techniques (visible–near‐infrared (Vis–NIR) and NIR) were utilized to acquire separate HSI data, which were then fused and analyzed toforecast the starch and alcohol contents during the fermentation of lees. Five preprocessing methods were first used to preprocess the Vis–NIR, NIR, and the fused Vis–NIR and NIR data, after which partial least squares regression models were established to determine the best preprocessing method. Following, competitive adaptive reweighted sampling, successive projection algorithm, and principal component analysis algorithms were used to extract the characteristic wavelengths to accurately predict the starch and alcohol levels. Finally, support vector machine (SVM)‐AdaBoost and XGBoost models were built based on the low‐level fusion (LLF) and intermediate‐level fusion (ILF) of single Vis–NIR and NIR as well as the fused data. The results showed that the SVM‐AdaBoost model built using the LLF data afterpreprocessing by standard normalized variable was most accurate for predicting the starch content, with an RP2$\ R_P^2$ of 0.9976 and a root mean square error of prediction (RMSEP) of 0.0992. The XGBoost model built using ILF data was most accurate for predicting the alcohol content, with an RP2$R_P^2$ of 0.9969 and an RMSEP of 0.0605. In conclusion, the analysis of fused data from distinct HSI technologies facilitates rapid and precise determination of the starch and alcohol contents in fermented grains. [ABSTRACT FROM AUTHOR]

Carbon sequestration in deep, unmineable coal seams is a viable strategy for carbon reduction. However, the impact of CO2 on coal mechanical performance poses safety concerns for a reservoir. This study proposes an integrated learning methodology that leverages experimental data involving CO2 immersion in various phases to evaluate the mechanical performance of coal seams during carbon sequestration. The approach integrates support vector regression (SVR) through the bagging method and employs a novel algorithm to optimize SVR. The model systematically assesses seven key factors, including coal rank, sample size, saturation medium, saturation time, saturation pressure, saturation temperature, and loading rate, to understand their influence on mechanical performance. The study identified saturation temperature, coal rank, and the saturated medium as pivotal elements affecting coal seam weakening. Evaluation metrics such as squared correlation coefficient (R2), mean absolute error, and root mean square error were employed for performance comparison between the polynomial model and the integrated model. The results demonstrate the superior performance of the integrated model, with R2 of 0.98, emphasizing its effectiveness in predicting coal seam strength weakening during carbon sequestration. These insights contribute to safety assessment of coalbed carbon sequestration practices. [ABSTRACT FROM AUTHOR]

Airborne sensing images harness the combined advantages of hyperspectral and high spatial resolution, offering precise monitoring methods for local-scale water quality parameters in small water bodies. This study employs airborne hyperspectral remote sensing image data to explore remote sensing estimation methods for total nitrogen (TN) and total phosphorus (TP) concentrations in Lake Dianshan, Yuandang, as well as its main inflow and outflow rivers. Our findings reveal the following: (1) Spectral bands between 700 and 750 nm show the highest correlation with TN and TP concentrations during the summer and autumn seasons. Spectral reflectance bands exhibit greater sensitivity to TN and TP concentrations compared to the winter and spring seasons. (2) Seasonal models developed using the Catboost method demonstrate significantly higher accuracy than other machine learning (ML) models. On the test set, the root mean square errors (RMSEs) are 0.6 mg/L for TN and 0.05 mg/L for TP concentrations, with average absolute percentage errors (MAPEs) of 23.77% and 25.14%, respectively. (3) Spatial distribution maps of the retrieved TN and TP concentrations indicate their dependence on exogenous inputs and close association with algal blooms. Higher TN and TP concentrations are observed near the inlet (Jishui Port), with reductions near the outlet (Lanlu Port), particularly for the TP concentration. Areas with intense algal blooms near shorelines generally exhibit higher TN and TP concentrations. This study offers valuable insights for processing small water bodies using airborne hyperspectral remote sensing images and provides reliable remote sensing techniques for lake water quality monitoring and management. [ABSTRACT FROM AUTHOR]

Due to the large intra-class differences between the same categories and the scale imbalance between different categories in the remote sensing image dataset, the semantic segmentation task presents the problem of small-scale object information loss, the imbalance between foreground and background, and simultaneously the background dominates, which seriously affects the performance of the network model. To solve the above problems, this paper proposes an efficient bilateral branch depth neural network model based on the U-Net depth neural network, named BBU-Net. Firstly, one branch of the network learns the distribution characteristics of the original data, and the other focuses on difficult samples. Then the two branches improve the representation and classification ability of the neural network by accumulating learning strategies. Finally, considering the geometric diversity of remote sensing images, this paper adopts test time augmentation and reflection padding strategies and proposes a balanced weighted loss function named CombineLoss to alleviate the imbalance in the training process. The depth neural network proposed in this paper was first tested on the Inria Aerial Image Labeling Dataset, and 87.53% of mean intersection over union and 97.4% of mean pixel accuracy were obtained, respectively. At the same time, to verify the model's complexity, the model proposed in this paper is compared with the neural network based on integrated learning. The comparison results show that the spatial complexity of the network proposed in this paper is much lower than the neural network obtained by integrated learning, and the parameters are also much smaller than the neural network based on integrated learning. Then use the satellite building dataset I in the WHU Building Dataset and mainstream semantic segmentation methods for multiple groups of comparative experiments. The experimental results show that the method proposed in this paper can effectively extract the semantic information of remote sensing images, significantly improve the imbalance of remote sensing image data, improve the performance of the network model, and achieve a good semantic segmentation effect, which fully proves the effectiveness of this method. [ABSTRACT FROM AUTHOR]

The article proposes the concept of assessing the level of optimal load modes in terms of tasks (flow of tasks) for two groups of students in the process of integrated education in the specialty 126 "Information systems and technologies" and specialty 022 "Web design", by using the toolkit of Markov chains and equations Kolmogorov. The proposed approach allows you to reproduce the dynamics of states and the results of the mutual influence of two social groups, which are represented by a contingent of students in the context of processing the flow of tasks. Possible options for research may include comparing the level of productivity of each group, the influence of factors affecting their work, as well as the calculation of probabilities of states of social groups over time and marginal probabilities. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Objective, accurate and fair development of research and effective performance prediction methodology for the construction of the talent team is the current needs of the new era of innovation and reform and development of university management, as well as the need to improve the quality of scientific research and teaching level of the talent team. OBJCTIVES: To address the problems of irrational principle of indicator selection, incomplete system and imprecise methodology in the current research on performance prediction of talent team building. METHODS: This paper proposes a talent team construction performance prediction method based on intelligent optimization algorithm improving neural network with integrated learning as the framework. First of all, through the analysis of the current talent team construction performance prediction influencing factors selection principles, analyze the talent team construction performance management process, select the talent team construction performance prediction influencing factors, and construct the talent team construction performance analysis system; then, with the integrated learning as a framework, improve the neural network through the internal search optimization algorithm to construct the talent team construction performance prediction model; finally, through the simulation experiments to analyze and verify the effectiveness and superiority of the proposed method. The effective type and superiority of the proposed method are verified. RESULTS: The results show that the proposed method satisfies the real-time requirements while improving the prediction accuracy. CONCLUSION: This paper addresses the lack of precision in forecasting the performance of the talent pipeline and the lack of a sound analytical system. [ABSTRACT FROM AUTHOR]

The paper considers the use of interdisciplinary connections in the lessons of English as a stimulating factor in learning. The authors explore how the integration of the content of various subjects with the English language curriculum can improve the learning of the material and stimulate the motivation of students. The article emphasizes the importance of using interdisciplinary connections in English lessons and offers pedagogical recommendations for teachers to help create a stimulating and integrated learning environment. In particular, the authors suggest using interdisciplinary connections to study grammar, vocabulary and phonetics of the English language through the context of other subjects such as music, literature or the shop class. This will lead to better understanding and memorising the material as well as develop an interest in the subject.

In order to solve the problem of excessive or insufficient dosing of coagulant in water plants, and to be compatible with and respond to the change of raw water quality in time, the intelligent dosage prediction model of coagulant with the integrated learning framework was constructed. In the data preprocessing stage, the turbidity removal rate was introduced to filter the original data set to reduce the proportion of unreasonable data in the training data. In characteristic engineering, the nonlinear relationship among inlet water quality,quantity and coagulant dosage was established by feedforward control theory. Meanwhile, the time sequence characteristics were obtained by autocorrelation and partial correlation coefficient analysis and used as model input. The system models were divided into long-term model, medium-term model, and short-term model. Each model used the integrated learning framework of Stacking. The system model outputted the predicted value of coagulant addition through weight distribution. The results showed that the introduction of time series features significantly improved the prediction performance of the model. With the integrated learning framework, the prediction and evaluation indexes MAPE and R2 of the mixed model dosage were 3.78% and 0.96 respectively, and the predicted dosage of coagulant was about 3.12% less than the actual value. It can provide a feasible solution for water plants to achieve accurate dosing and reduce drug consumption

To address the low recognition accuracy of generic emotion recognition models when faced with different individuals, a multimodal individual emotion recognition technique based on joint labelling with integrated learning and clustering was proposed. The method first trained a generic emotion recognition model based on a public dataset, then anallysed the distributional differences between the data in the public dataset and the unlabelled data of individuals, and established a cross-domain model for predicting and labelling pseudo-labels of individual data. At the same time, the individual data were weighted clustered and labelled with cluster labels, and the cluster labels were used to jointly label with pseudo-labels, and high confidence samples were screened to further train the generic model to obtain a personalized emotion recognition model. Using this method to annotate these data with the experimentally collected data of 3 emotions from 3 subjects, the final optimized personalized model achieved an average recognition accuracy of more than 80% for the 3 emotions, which was at least a 35% improvement compared to the original generic model.

Distance education offers an alternative to traditional teaching methods, particularly online courses. During the pandemic, the role of information and communication technologies in the field of education has increased and is used to organize both synchronous and asynchronous learning. However, not all information and communication technology (ICT) tools and platforms provide the necessary functionality for developing online courses in the direction of language integration. This article discusses the issues of creating a multilingual online course of physics in English with the necessary language support and additional functions for the development of students' academic language. The study used such methods as a survey, focus groups, analytical and descriptive methods, and empirical research. The key features of the developed course are the use of various types of content and methods for assessing both subject and language knowledge and skills. The presented survey results, data on summative assessment (SE), and trial testing show the effectiveness of the developed course modules. The conclusions and results described in the article will be useful to teachers of natural sciences in general education and private schools, especially those using international programs, deputy directors, as well as teachers who teach their subjects in a second or third language. [ABSTRACT FROM AUTHOR]

Copyright of Industrial Water Treatment is the property of CNOOC Tianjin Chemical Research & Design Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bulletin of Ablai Khan KazUIRandWL: Series 'Pedagogical Sciences' is the property of Kazakh Ablai Khan University of International Relations & World Languages and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Biochar is a biomaterial obtained by pyrolysis with high porosity and high specific surface area (SSA), which is widely used in several fields. The yield of biochar has an important effect on production cost and utilization efficiency, while SSA plays a key role in adsorption, catalysis, and pollutant removal. The preparation of biochar materials with better SSA is currently one of the frontiers in this research field. However, traditional methods are time consuming and laborious, so this paper developed a machine learning model to predict and study the properties of biochar efficiently for engineering through cross-validation and hyper parameter tuning. This paper used 622 data samples to predict the yield and SSA of biochar and selected eXtreme Gradient Boosting (XGBoost) as the model due to its excellent performance in terms of performance (yield correlation coefficient R2 = 0.79 and SSA correlation coefficient R2 = 0.92) and analyzed it using Shapley Additive Explanation. Using the Pearson correlation coefficient matrix revealed the correlations between the input parameters and the biochar yield and SSA. Results showed the important features affecting biochar yield were temperature and biomass feedstock, while the important features affecting SSA were ash and retention time. The XGBoost model developed provides new application scenarios and ideas for predicting biochar yield and SSA in response to the characteristic input parameters of biochar. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The construction of English evaluation methods in colleges and universities, as the essential part of English teaching in colleges and universities, is conducive to the improvement of the quality of English teaching in colleges and universities, which makes the existing English teaching more objective and reasonable, and makes the means of English teaching rich in science. OBJECTIVES: Aiming at the current wisdom teaching evaluation design methods exist evaluation indexes exist objectivity is not strong, accuracy is poor, single method and other problems. METHODS:Proposes a college English teaching evaluation method based on a deep learning network. First, the evaluation index system of English in colleges and universities is constructed by analyzing the principle of selecting evaluation indexes of English in colleges and universities; then, the deep learning network is improved through self-coder and integrated learning methods to construct the evaluation model of English teaching in colleges and universities; finally, the effectiveness and efficiency of the proposed method is verified through simulation experiment analysis. RESULTS: The results show that the proposed method improves the accuracy of the evaluation model. CONCLUSION: Solved the problems of low evaluation accuracy and non-objective system indexes of English teaching evaluation methods in colleges and universities. [ABSTRACT FROM AUTHOR]

Cyber-Physical Systems (CPS) has emerged as a paradigm that connects cyber and physical worlds, which provides unprecedented opportunities to realize intelligent applications such as smart home, smart cities, and smart manufacturing. However, CPS faces a great number of information security challenges (e.g., attacks) due to the integration of CPS as well as the human behaviors and interactions. Therefore, accurate and real-time attack detection and identification are essential to ensure information security and reliability of CPS. In this paper, we propose a novel integrated learning method that accurately detects an attack of a CPS system and then identifies the attack type in real time. Specifically, we consider a One-Class Support Vector Machine (OCSVM) model that only relies on the data from the normal state for training to achieve a real-time and effective detection of a CPS system state (i.e., normal or under-attack). If the system is detected to be under-attack, we then develop a Pairwise Self-supervised Long Short-Term Memory (PSLSTM) approach to identify the attack type, which aims to accurately distinguish the known attack types and discover unknown new attacks. Lastly, experimental results show the proposed method achieves promising performances compared with conventional and state-of-the-art learning-based benchmarks. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Intelligent Science & Technology is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the problem that the channel estimation accuracy of the traditional urban communication system is not high, the author proposes the channel estimation of the urban 5G communication system based on the improved particle optimization algorithm. This method converts the channel estimate into a regression fit and adjusts the fit. Focusing on regression fitting problems, big data models are used to display offline data, study channel nonlinearities, and obtain initial channel prediction models. To solve the adaptive problem, the author collects real-time teaching data in a real online learning mode and integrates blended learning to update the model, to avoid the problem of overspending on offline training. Offline tests show that the performance of the channel estimation model is the best for different channels. As the signal-to-noise ratio increases, the MSE value is stable at around 1200. Conclusion: The channel estimation method can produce different characteristics of channel estimation in different situations and improve the signal recovery function of the communication system. [ABSTRACT FROM AUTHOR]

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As higher education institutions look to educate and graduate degree earners that have the skills and knowledge necessary to design, communicate, and collaborate in ways that allow for innovative solutions to complex socio-technical challenges, new approaches to educational efforts are being considered and implemented. Institutional responses have included new courses that have a transdisciplinary focus and embedded course experiences that use problem-based approaches coupled with cross-disciplinary team exposure. Often these initiatives have a singular thematic focus (i.e., sustainability). Little is known about the efficacy of transdisciplinary learning initiatives, especially those that encompass a breadth of thematic areas, as it relates to development of complex thinking skills and whether these learning environments provide for similar benefits across student majors. This study, conducted at a Research I university, was designed to address this gap in the literature by examining whether a university level learning initiative using transdisciplinary approaches was achieving desired goals to advance undergraduate students' complex thinking skills. Findings among the respondents (n = 592) indicate that the initiative is advancing fundamental complex thinking skills in that specific courses allow students to learn about other disciplines and provide exposure to different disciplinary perspectives. However, results reveal that across all majors courses would benefit from increased real-world problem-based exposure and opportunities to interact with community stakeholders. Additionally, results suggest that undergraduates may need opportunities to develop a deeper understanding of the complexities that exist in cross-disciplinary collaboration, including how to develop integrated solutions that leverage the strengths of technical and non-technical approaches. [ABSTRACT FROM AUTHOR]

The main objective of this study is to develop and evaluate an effective integrated learning approach for the automatic classification of Municipal Solid Waste using the TrashBox dataset, comprising 17,785 images, to improve the sorting of recyclable waste materials, reduce landfill usage, and promote sustainable environmental practices. Initially, four deep learning models—DenseNet161, ResNet152, and MobileNetV3 variants—are explored to determine the most suitable feature extraction method. During the feature selection phase, three metaheuristic algorithms—Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), and Harris Hawk Optimization (HHO)—are applied to filter out irrelevant features and retain significant ones. These selected features are then fed into machine learning classifiers—Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbor (KNN)—for final predictions. The DenseNet161-HHO-SVM combination outperforms other models in this study, achieving the highest accuracy and lowest execution time. This integrated approach also demonstrates superior performance (97.45%) compared to previous state-of-the-art models on the same dataset, with the data processing and method integration phases having substantial impacts.

With the increasing demand for DC transmission from new energy sources and the increasing DC load, the original AC distribution network system cannot meet the demand for power transmission, and AC-DC distribution network has become one of the development directions of future distribution network. When photovoltaic system is connected to AC/DC distribution system, unplanned island operation may occur on both AC and DC sides of the system. When unplanned islanding occurs in the system, maintenance personnel and system equipment fail to detect islanding in time and operate normally, which may pose a great threat to the safety of equipment and personnel. Therefore, it is necessary to accurately identify the operating state of the system, and then provide accurate judgment information for the action of the micro-switch to operate the photovoltaic system with islanding phenomenon through the micro-switch integrated with islanding detection algorithm, so as to ensure the safety and stability of the distribution system and load. In order to identify the running state of the system reliably, this paper analyzes the changes of the system electrical quantity before and after the islanding, and based on this, puts forward an islanding detection method. First of all, this paper uses a variety of data preprocessing techniques to clean and extract the features of the original data, and selects six island characteristic indicators such as voltage, current and output active power as detection features to generate a feature vector set to improve the quality and accuracy of the data. Secondly, this paper adopts three classification algorithms, including KNN, random forest and XGBoost, to integrate and learn the model, so as to improve the accuracy and robustness of the islanding detection task. Finally, it is verified by experiments that the islanding detection method based on multi-classification fusion model proposed in this paper has high fault detection accuracy and robustness, and has a wide application prospect and popularization value in practical application, which can provide performance improvement and stability guarantee for micro-cluster switches.

Power loads usually have multiple cycles, and the traditional forecasting methods only use the historical loads under various types of cycles together as input features to construct the forecasting model, ignoring the deep features under multiple cycles. The simultaneous inputs will lead to the cycles overlapping and influencing each other, which will be difficult to deal with when building the model. Therefore, this paper proposes a GRO-Bagging day-ahead power curve forecasting model based on multi-cycle feature extraction. First, the multi-periodicity of the power load is analyzed. The one-dimensional time series is converted to two-dimensional according to the multiple cycles of power load. Then, the multi-periodic feature extraction is performed by a multi-size convolutional feature extraction layer with parallel selected based on the data characteristics and modeling mechanism, and Bootstrap Aggregating (Bagging) method is used to construct different prediction models for the datasets containing different periodic features; finally, Gold Rush Optimization (GRO) algorithm is introduced and improved by using the Tent chaotic mapping and elite strategy, and the improved optimization algorithm is used for the weight optimization of the model, the error feedback mechanism is introduced to achieve the weight dynamic Updating. To prove the superiority of the proposed model, a series of comparison experiments and ablation experiments are carried out on real datasets, and the results show that the proposed method has higher prediction accuracy, and prove that the multi-period feature extraction and dynamic weighting methods have a positive and active effect on load prediction.

To address the challenges posed by a large number of disaster-waiver-affected users and the complexities of scaling centralized algorithms for rapidly restoring emergency communication services, the paper proposes a distributed intent-based optimization architecture based on multi-agent reinforcement learning. This approach aims to mitigate service discrepancies and dynamics among users. In the network feature layer, a distributed K-sums clustering algorithm considers variations in user services. Each UAV base station autonomously and minimally adjusts the local network structure based on user requirements. It selects user features from the cluster center as input states for the multi-agent reinforcement learning neural network. In the trajectory regulation layer, the paper introduces a multi-agent maximum entropy reinforcement learning (MASAC) algorithm. The UAV base station, acting as an intelligent node, governs its flight trajectory within the framework of “distributed training – distributed execution.” The paper incorporates techniques such as integrated learning and curriculum learning to enhance training stability and convergence speed. Simulation results demonstrate the effectiveness of our distributed K-sums clustering algorithm in terms of load efficiency and cluster balance, outperforming the traditional K-means algorithm. Additionally, the UAV base station trajectory control algorithm based on MASAC significantly reduces communication interruptions, enhances network spectral efficiency, and surpasses existing reinforcement learning methods.

The development of the digital competence is a lifelong learning goal, which will remain stable, taking into account the digitization of societies and global economies. In this context and unlike the other European states, in the Romanian curriculum, the digital competence can not be found among the acquisitions of learning at the primary education level. In the absence of some clearly formulated learning objectives, which could be integrated into the development of digital competence, the teaching staff has the option to adapt the learning activities or to propose some new ones, so that the projection of the lessons ensures a teaching approach adequate to the acquisition of digital knowledge and skills. This paper highlights the possibilities of developing the specific competences of the objects in the current education framework plan for primary education, in a prototype approach to support the premise of their integration in the development of digital competence.

The distribution network line loss rate is a crucial factor in improving the economic efficiency of power grids. However, the traditional prediction model has low accuracy. This study proposes a predictive method based on data preprocessing and model integration to improve accuracy. Data preprocessing employs dynamic cleaning technology with machine learning to enhance data quality. Model integration combines long short-term memory (LSTM), linear regression, and extreme gradient boosting (XGBoost) models to achieve multi-angle modeling. This study employs regression evaluation metrics to assess the difference between predicted and actual results for model evaluation. Experimental results show that this method leads to improvements over other models. For example, compared to LSTM, root mean square error (RMSE) was reduced by 44.0% and mean absolute error (MAE) by 23.8%. The method provides technical solutions for building accurate line loss monitoring systems and enhances power grid operations. [ABSTRACT FROM AUTHOR]

In recent days, integrated learning emerges as a potent pedagogical approach, intertwining diverse subjects to foster comprehensive education for millennial learners. Its benefits span across enhanced learning outcomes, improved critical thinking, creative problem solving, and engaged learning. The research study aims to create an effective integrated learning experience for the undergraduate students of information technology program. Around 65 students of fifth semester B.Tech IT program and three faculty members have participated in the research study. Project-Based Learning (PBL) has been adapted to creating meaningful cross-curricular experiences. The proposed PBL enables the learners to develop simulations for various cryptographic algorithms which were learnt in the course on Information security. The project also intertwines the concepts covered in other courses like Web Technologies, Software Testing and System Thinking. The PBL has been carefully designed with focus on problem statements, learning goals, project milestones, calendar and rubrics for assessment. Simulations developed by the learners in teams are integrated to create a complete software package which could serve as a virtual lab for cryptography and information security course. The experimental study on integrated learning has resulted in a usable educational software product for teaching cryptographic algorithms. Also, there is a significant enhancement in the attainment of course outcomes and satisfaction index of the learners. The positive outcome in learning achievements and student engagement affirm integrated learning's value. Various plans to address the implementation challenges in integrated learning are also discussed in the research article. The authors believe that the manuscript will serve as a beginner's guide to academicians wishing to practice integrated learning in their classrooms. [ABSTRACT FROM AUTHOR]