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Hybrid teaching, which integrates traditional in-person learning based on students’ perspectives where online learning offers a flexible approach to education, combines the benefits of technology with face-to-face interactions. Moreover, teaching and learning in a hybrid way met several challenges for both teachers and learners, including technological problems, time management, communication difficulties, and assessment complexities. This systematic review investigates six main research questions: (1) What pedagogical frameworks are used in hybrid teaching and learning? (2) How can we enhance students’ engagement in hybrid teaching and learning? (3) What is the impact of technological integration on hybrid learning scenarios, both for students and teachers? (4) How do training and support measures influence the willingness and ability of university teachers to implement hybrid teaching formats? (5) How do formative assessment and feedback methods in hybrid learning environments enable teachers to effectively monitor student progress and provide tailored support? (6) How does the implementation of hybrid learning affect student learning outcomes? This study identifies the following key themes: technological integration, pedagogical innovation, faculty support, student engagement, assessment practices, and learning outcomes. Our contribution of this literature review is related to teaching and learning by showing teachers the most appropriate way to avoid the challenges encountered when teaching in a hybrid way. These include strong technology integration, innovative pedagogical strategies, strong academic development and support, active student engagement, effective assessment practices, and positive learning outcomes.

Due to the COVID-19 pandemic, many nursing students are being taught remotely. Remote learning has drawbacks, such as decreased motivation for learning and difficulties conveying the instructor’s intentions. Strategies that compensate for the shortcomings of remote learning should be identified. This study aimed to evaluate the understanding of the knowledge use and awareness of negotiation methods through cases and teaching tools in nursing student classes on environmental assessment and daily life support, and to examine whether supplementary assistance can compensate for the drawbacks of remote learning. This study used a mixed-method design, and included 59 second-year nursing students attending an environmental assessment course in 2021. Students’ knowledge use and awareness of negotiation methods were evaluated using self-assessment worksheets before and after the class. The pre- and post-class scores were compared using the Wilcoxon signed-rank test. The mean knowledge score increased significantly during the study period (p < 0.001). Students acquired awareness of how to use the knowledge gained during class and negotiation awareness by observing role play, factors that strengthen motivation when learning alone. This study provides insight into the potential of class supplements to compensate for the deficits of remote learning. Supplementing the shortcomings of remote learning should be a priority and may be a focal point of hybrid learning.

The main promise of new, digitally enabled and hybrid learning environments is to enable future-ready knowledge workers by equipping them with business and digital competences. However, business education (BE) research often focuses on the problems of instructional design and individual disciplines, rather than on the challenges of developing a holistic knowledge and competences required to ensure students’ long-term employability. This paper, to address this gap, approaches BE as a knowledge dynamics field that consists of rational, emotional and spiritual knowledge and proposes a related framework to serve as a guide for developing and analyzing a hybrid learning environment (HLE) that would support BE. Then, it uses the developed framework in an interview-based study to understand the students’ perceptions of how the implementation of an HLE in postgraduate course stimulated knowledge dynamics for BE. The results show that the HLE stimulated different aspects of knowledge due to the diversity of modes of learning-Face-to-Face (F2F) and online, the diversification of learning sources and the internationalization of the course-level curriculum. These results pave the direction for teachers to use the knowledge framework as a compass for future implementations and evaluations of similar methods.

Abstract - Students must have academic writing competencies in order to express scientific ideas and attitudes through scientific papers. Therefore, learning activities are needed according to the times. Various information and communication technology devices can be used to access learning resources and materials. Learning activities can be presented by utilizing information and communication technology devices, including implementing online learning. Hybrid learning presents a variety of learning materials by utilizing the advantages of face to face meetings and online learning. The purpose of this study is to produce an academic writing learning model based on hybrid learning. The method used is development with the following research procedures: design, production, and product testing. The results were obtained by academic writing learning models in higher education based on hybrid learning.

Air pollution poses a pressing global challenge, particularly in rapidly industrializing nations like China where deteriorating air quality critically endangers public health and sustainable development. To address the heterogeneous patterns of air pollution across diverse geographical and climatic regions, this study proposes a novel CNN-LSTM-KAN hybrid deep learning framework for high-precision Air Quality Index (AQI) time-series prediction. Through systematic analysis of multi-city AQI datasets encompassing five representative Chinese metropolises—strategically selected to cover diverse climate zones (subtropical to temperate), geographical gradients (coastal to inland), and topographical variations (plains to mountains)—we established three principal methodological advancements. First, Shapiro–Wilk normality testing (p < 0.05) revealed non-Gaussian distribution characteristics in the observational data, providing statistical justification for implementing Gaussian filtering-based noise suppression. Second, our multi-regional validation framework extended beyond conventional single-city approaches, demonstrating model generalizability across distinct environmental contexts. Third, we innovatively integrated Kolmogorov–Arnold Networks (KANs) with attention mechanisms to replace traditional fully connected layers, achieving enhanced feature weighting capacity. Comparative experiments demonstrated superior performance with a 23.6–59.6% reduction in Root-Mean-Square Error (RMSE) relative to baseline LSTM models, along with consistent outperformance over CNN-LSTM hybrids. Cross-regional correlation analyses identified PM2.5/PM10 as dominant predictive factors. The developed model exhibited robust generalization capabilities across geographical divisions (R2 = 0.92–0.99), establishing a reliable decision-support platform for regionally adaptive air quality early-warning systems. This methodological framework provides valuable insights for addressing spatial heterogeneity in environmental modeling applications.

This study proposes a new hybrid machine learning (ML) model for the early and accurate diagnosis of heart disease. The proposed model is a combination of two powerful ensemble ML models, namely ExtraTreeClassifier (ETC) and XGBoost (XGB), resulting in a hybrid model named ETCXGB. At first, all the features of the utilized heart disease dataset were given as input to the ETC model, which processed it by extracting the predicted probabilities and produced an output. The output of the ETC model was then added to the original feature space by producing an enriched feature matrix, which is then used as input for the XGB model. The new feature matrix is used for training the XGB model, which produces the final result that whether a person has cardiac disease or not, resulting in a high diagnosis accuracy for cardiac disease. In addition to the proposed model, three other hybrid DL models, such as convolutional neural network + recurrent neural network (CNN-RNN), convolutional neural network + long short-term memory (CNN-LSTM), and convolutional neural network + bidirectional long short-term memory (CNN-BLSTM), were also investigated. The proposed ETCXGB model improved the prediction accuracy by 3.91%, while CNN-RNN, CNN-LSTM, and CNN-BLSTM enhanced the prediction accuracy by 1.95%, 2.44%, and 2.45%, respectively, for the diagnosis of cardiac disease. The simulation outcomes illustrate that the proposed ETCXGB hybrid ML outperformed the classical ML and DL models in terms of all performance measures. Therefore, using the proposed hybrid ML model for the diagnosis of cardiac disease will help the medical practitioner make an accurate diagnosis of the disease and will help the healthcare society decrease the mortality rate caused by cardiac disease.

Brain tumor analysis is essential to the timely diagnosis and effective treatment of patients. Tumor analysis is challenging because of tumor morphology factors like size, location, texture, and heteromorphic appearance in medical images. In this regard, a novel two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs). In the first phase, a novel deep-boosted features space and ensemble classifiers (DBFS-EC) scheme is proposed to effectively detect tumor MRI images from healthy individuals. The deep-boosted feature space is achieved through customized and well-performing deep convolutional neural networks (CNNs), and consequently, fed into the ensemble of machine learning (ML) classifiers. While in the second phase, a new hybrid features fusion-based brain-tumor classification approach is proposed, comprised of both static and dynamic features with an ML classifier to categorize different tumor types. The dynamic features are extracted from the proposed brain region-edge net (BRAIN-RENet) CNN, which is able to learn the heteromorphic and inconsistent behavior of various tumors. In contrast, the static features are extracted by using a histogram of gradients (HOG) feature descriptor. The effectiveness of the proposed two-phase brain tumor analysis framework is validated on two standard benchmark datasets, which were collected from Kaggle and Figshare and contain different types of tumors, including glioma, meningioma, pituitary, and normal images. Experimental results suggest that the proposed DBFS-EC detection scheme outperforms the standard and achieved accuracy (99.56%), precision (0.9991), recall (0.9899), F1-Score (0.9945), MCC (0.9892), and AUC-PR (0.9990). The classification scheme, based on the fusion of feature spaces of proposed BRAIN-RENet and HOG, outperform state-of-the-art methods significantly in terms of recall (0.9913), precision (0.9906), accuracy (99.20%), and F1-Score (0.9909) in the CE-MRI dataset.

Terrestrial biosphere models offer a comprehensive view of the global carbon cycle by integrating ecological processes across scales, yet they introduce significant uncertainties in climate and biogeochemical projections due to diverse process representations and parameter variations. For instance, different soil water limitation functions lead to wide productivity ranges across models. To address this, we propose the Differentiable Land Model (DifferLand), a novel hybrid machine learning approach replacing unknown water limitation functions in models with neural networks (NNs) to learn from data. Using automatic differentiation, we calibrated the embedded NN and the physical model parameters against daily observations of evapotranspiration, gross primary productivity, ecosystem respiration, and leaf area index across 16 FLUXNET sites. We evaluated six model configurations where NNs simulate increasingly complex soil water and photosynthesis interactions against test data sets to find the optimal structure‐performance tradeoff. Our findings show that a simple hybrid model with a univariate NN effectively captures site‐level water and carbon fluxes on a monthly timescale. Across a global aridity gradient, the magnitude of water stress limitation varies, but its functional form consistently converges to a piecewise linear relationship with saturation at high water levels. While models incorporating more interactions between soil water and meteorological drivers better fit observations at finer time scales, they risk overfitting and equifinality issues. Our study demonstrates that hybrid models have great potential in learning unknown parameterizations and testing ecological hypotheses. Nevertheless, careful structure‐performance tradeoffs are warranted in light of observational constraints to translate the retrieved relationships into robust process understanding. Plain Language Summary Terrestrial carbon cycles simulations commonly focus on either describing the ecological processes with physical yet empirical equations or capturing the statistical relationships between variables using data‐driven techniques. Both approaches have their advantages and disadvantages. Process‐based simulations are grounded in scientific principles but may be inaccurate due to imperfect knowledge of the equations. Machine‐learning techniques can potentially capture the complex relationships between environmental variables but can be hard to extrapolate. In this study, we combine the two approaches into a hybrid model by embedding a set of neural networks within a process‐based model. We tested the model at different locations to study whether it can learn how plants respond to water limitations. The results showed the hybrid modeling approach can successfully retrieve the functional relationships between ecological variables. In addition, the overall performance of the hybrid model improved compared to the baseline model due to increased structural flexibility. We envision such a hybrid approach to help in the presence of imperfect knowledge of the governing equations in terrestrial carbon simulations. Instead of prescribing uncertain governing equations for the unknown ecological relationships, we can let the hybrid model learn these functional relationships from data, while preserving the temporal consistency of the model. Key Points An automatically differentiable hybrid model is developed to learn parameters and functional relationships in land carbon and water cycles Neural network emulators simulate ecological dynamics well but risk equifinality with limited data due to increased degrees of freedom Monthly soil water impacts on GPP and ET are well‐captured by piecewise linear functions, but finer time effects may need more complexity

In recent times, a phishing attack has become one of the most prominent attacks faced by internet users, governments, and service-providing organizations. In a phishing attack, the attacker(s) collects the client’s sensitive data (i.e., user account login details, credit/debit card numbers, etc.) by using spoofed emails or fake websites. Phishing websites are common entry points of online social engineering attacks, including numerous frauds on the websites. In such types of attacks, the attacker(s) create website pages by copying the behavior of legitimate websites and sends URL(s) to the targeted victims through spam messages, texts, or social networking. To provide a thorough understanding of phishing attack(s), this paper provides a literature review of Artificial Intelligence (AI) techniques: Machine Learning, Deep Learning, Hybrid Learning, and Scenario-based techniques for phishing attack detection. This paper also presents the comparison of different studies detecting the phishing attack for each AI technique and examines the qualities and shortcomings of these methodologies. Furthermore, this paper provides a comprehensive set of current challenges of phishing attacks and future research direction in this domain.