Apple maturity detection algorithms based on deep learning typically involve a large number of parameters, resulting in high computational costs, long processing times, and dependence on high computational power graphics processing units (GPUs). This paper proposes an improved YOLOv8 model to address the issues related to the maturity detection of Fuji apples grown in China using image‐based methods. The model was optimized in several ways according to the characteristics of apple targets and scenes. First, a lightweight MobileNetV3 is used as the backbone network, replacing the original CSPDarknet‐53 backbone network, which reduces the model parameters and computational complexity and increases the inference speed. Second, by introducing the efficient multiscale attention (EMA) module and using the bidirectional feature pyramid network (BiFPN) in the neck part, the model enhances the extraction capability of important features and suppresses redundant features, thus improving the model’s generalization ability. Experimental results show that the size of the model is 2.6 megabytes. On the apple dataset, its precision, recall, F1 score, and mean average precision reach 90.2%, 88.5%, 89.3%, and 91.3%, respectively, with improvements of 4.3%, 3.2%, 3.7%, and 2.6% compared to the original model. Based on this model, an Android application has been developed for real‐time apple maturity detection. The improved model proposed in this paper achieves real‐time apple target recognition and maturity detection, providing quick and accurate target recognition guidance for the mechanical automatic harvesting of apples.

This study presents a holistic level of sustainability in the context of green architecture supply chains by integrating GSCM and GHRM through an integrated decision‐making framework. The study applies a hybrid approach of neutrosophic fuzzy TOPSIS and fault tree analysis (FTA) with data mining to identify and rank critical barriers relating to sustainability, including insufficient government funding and technology challenges. This model offers practical recommendations for policymakers and practitioners aiming to improve green architecture considering GSCM and GHRM on supply chains. The analysis underscores the need for enhanced targeted policies and engagement from various stakeholders and the integration of digital technologies to strengthen sustainability efforts in the industry.

This paper investigates the input delay analysis and H ∞ control problem for networked control systems with finite‐time stochastic boundedness (FTSB). First, a novel control scheme is used to handle the Markovian jump parameters, and an event‐triggered rule is introduced to a networked control system with FTSB, which can ensure the control performance of the system and effectively improve the resource utilization of the networked control system. Simultaneously, a more accurate expression for input delay compared to traditional methods is obtained. Then, the sufficient condition for the networked control system to have FTSB is derived. Additionally, an H ∞ state feedback controller for the stochastic networked control system with FTSB performance is obtained. Finally, an illustrative example is provided to verify the effectiveness of the method proposed in this paper, especially the good control effect of the H ∞ state feedback controller.

The complexity of brain tumors highlights the critical need for advanced computer‐aided diagnosis (CAD) tools to support surgeons in clinical decision‐making and improve patient outcomes. This paper introduces a novel deep learning model for the multiclass classification of brain tumors using magnetic resonance imaging (MRI), offering significant advancements in feature extraction and classification accuracy. The proposed model comprises three key components: (1) a fine‐tuned EfficientNetB7 convolutional neural network (CNN), adapted through transfer learning by freezing the initial layers and retraining subsequent layers to optimize feature extraction from MR images; (2) a channel attention module that refines extracted feature maps, emphasizing essential features for accurate tumor detection; and (3) a fully connected classifier, optimized through grid search, to achieve precise multiclass tumor classification. Additionally, hyperparameter tuning and data augmentation techniques enhance generalization and model robustness. Experimental results confirm the model’s superior performance, outperforming recent approaches in multiclass and binary classification scenarios, underscoring its potential to advance brain tumor diagnosis and treatment.

This study introduces a novel hybrid deep learning framework to enhance hotspot detection in photonic crystal design, addressing challenges in accuracy and overfitting. By integrating Capsule Networks (CapsNet) and transformer architectures with ensemble learning and data augmentation, the proposed approach optimizes the identification of wavelength propagation discrepancies in photonic structures. Experiments conducted on the ICCAD‐2012 benchmark dataset demonstrate that the ensemble model achieves 90% test accuracy, outperforming standalone CapsNet and transformer models while reducing overfitting. The model also demonstrates strong classification consistency, with an F1‐score of 90% and a G‐mean of 90%, indicating robust performance across precision–recall balance and class‐wise sensitivity–specificity harmony. The framework’s success in balancing performance and generalization highlights its potential to streamline photonic device design for applications in sensing, telecommunications, and energy harvesting. This work bridges advanced machine learning techniques with photonic engineering, offering a scalable and efficient solution for complex light‐matter interaction analysis.

Correct detection of plant diseases is critical for enhancing crop yield and quality. Conventional methods, such as visual inspection and microscopic analysis, are typically labor‐intensive, subjective, and vulnerable to human error, making them infeasible for extensive monitoring. In this study, we propose a novel technique to detect tomato leaf diseases effectively and efficiently through a pipeline of four stages. First, image enhancement techniques deal with problems of illumination and noise to recover the visual details as clearly and accurately as possible. Subsequently, regions of interest (ROIs), containing possible symptoms of a disease, are accurately captured. The ROIs are then fed into K‐means clustering, which can separate the leaf sections based on health and disease, allowing the diagnosis of multiple diseases. After that, a hybrid feature extraction approach taking advantage of three methods is proposed. A discrete wavelet transform (DWT) extracts hidden and abstract textures in the diseased zones by breaking down the pixel values of the images to various frequency ranges. Through spatial relation analysis of pixels, the gray level co‐occurrence matrix (GLCM) is extremely valuable in delivering texture patterns in correlation with specific ailments. Principal component analysis (PCA) is a technique for dimensionality reduction, feature selection, and redundancy elimination. We collected 9014 samples from publicly available repositories; this dataset allows us to have a diverse and representative collection of tomato leaf images. The study addresses four main diseases: curl virus, bacterial spot, late blight, and Septoria spot. To rigorously evaluate the model, the dataset is split into 70%, 10%, and 20% as training, validation, and testing subsets, respectively. The proposed technique was able to achieve a fantastic accuracy of 99.97%, higher than current approaches. The high precision achieved emphasizes the promising implications of incorporating DWT, PCA, GLCM, and ANN techniques in an automated system for plant diseases, offering a powerful solution for farmers in managing crop health efficiently.

Employing the lens of Actor‐Network Theory (ANT), this paper explores university educators’ perceptions of Gen‐AI and its potential impact on education through a qualitative research approach. It focuses on how educators perceive generative AI technologies and how these technologies influence their teaching practices and role identification. The study reveals that multiple factors shape teachers’ acceptance and utilization of AI, encompassing personal attitudes, student responses, teaching environments, and institutional policies. The interaction between teachers and AI is conceptualized as part of a human‐machine interaction network, where AI transcends being merely a teaching tool and is attributed human‐like qualities such as a wise mentor and learning companion. This research underscores that the realization of human‐AI integration necessitates a synergistic development between educators and technology, coupled with the concerted efforts of other actors within the educational ecosystem. The paper concludes by discussing the implications and challenges of human‐AI integration for educational reform.

Inspired by the genetic evolution mechanism of DNA, a hybrid DNA Gray Wolf Optimizer (hDNA‐GWO) is proposed to develop an accurate kinetic model. This algorithm incorporates innovative DNA encoding, selection, crossover, and mutation operators inspired by genetic processes. We adopt the roulette‐wheel method to select individuals with greater environmental adaptability from the current population to form the next population. The crossover operation involves swapping gene segments between paired chromosomes to create new individuals and maintain the population diversity. The mutation operator can maintain the diversity of the population, avoid the phenomenon of “premature” convergence, and effectively improve the local search capability. The performance of hDNA‐GWO is investigated on typical benchmark functions compared to GWO, PSO, GWO‐PSO, and GWO‐GA. In addition, the superior search capabilities of our model are validated by kinetic parameter estimation using experimental data from supercritical water oxidation processes. The results indicate that the hDNA‐GWO can overcome premature convergence and obtain higher‐quality global optimal solutions.

The field of mathematical inequalities has exerted a profound influence across a multitude of scientific disciplines, making it a captivating and expansive domain ripe for research investigation. This article offers estimations for the Slater difference through the application of the concept of convexity. We present a diverse type of applications that stem from the main findings related to power means, Zipf–Mandelbrot entropy, and within the field of information theory. Our main tools for deriving estimates for the Slater difference involve the triangular inequality, the definition of the convex function, and the well-established Jensen inequality.

The uncertainty of parameters will have a significant impact on slope stability, where sensitivity analysis is a commonly used method in uncertainty research. However, traditional sensitivity analysis method costs much computation time. When calculating the sensitivity index of one parameter, all other parameters are taken as fixed values, and the uncertainty of all parameters cannot be considered simultaneously. Therefore, the variance-based and the moment-independent global sensitivity analysis (GSA) methods are both introduced to determine the influence of geotechnical parameters on slope stability in this study. To solve the importance index of GSA, the least angle regression algorithm, the kernel density estimation, and orthogonal polynomial estimation methods are developed to obtain variance-based importance index and the moment-independent importance index, respectively. The proposed methods allow all variables to change simultaneously within their variation range and have high computational efficiency. The results are in good at with those obtained by the variance-based Monte Carlo simulation method, which is considered as the exact solution forobtaining the importance index. The influence of the correlation between the shear strength parameters (c and φ) on the importance index is also studied, which indicates that the negative correlation will have a great impact on the importance index, which in turn affects the safety assessment of slope. Three engineering cases have been studied for engineering application, and the compared results indicate that the impact of the geotechnical parameters uncertainty on the safety factor (Fs) and failure probability (pf) are different. Therefore, the approaches based on GSA which can integrate the Fs with pf will be a promising approach for slope stability evaluation.