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.

The development of low-temperature solid oxide fuel cells (LT-SOFCs) is of significant importance for realizing the widespread application of SOFCs. This has stimulated a substantial materials research effort in developing high oxide-ion conductivity in the electrolyte layer of SOFCs. In this context, for the first time, a dielectric material, CaCu ₃ Ti ₄ O ₁₂ (CCTO) is designed for LT-SOFCs electrolyte application in this study. Both individual CCTO and its heterostructure materials with a p -type Ni _0.8 Co _0.15 Al _0.05 LiO _{2− δ} (NCAL) semiconductor are evaluated as alternative electrolytes in LT-SOFC at 450–550 °C. The single cell with the individual CCTO electrolyte exhibits a power output of approximately 263 mW cm ⁻² and an open-circuit voltage (OCV) of 0.95 V at 550 °C, while the cell with the CCTO–NCAL heterostructure electrolyte capably delivers an improved power output of approximately 605 mW cm ⁻² along with a higher OCV over 1.0 V, which indicates the introduction of high hole-conducting NCAL into the CCTO could enhance the cell performance rather than inducing any potential short-circuiting risk. It is found that these promising outcomes are due to the interplay of the dielectric material, its structure, and overall properties that led to improve electrochemical mechanism in CCTO–NCAL. Furthermore, density functional theory calculations provide the detailed information about the electronic and structural properties of the CCTO and NCAL and their heterostructure CCTO–NCAL. Our study thus provides a new approach for developing new advanced electrolytes for LT-SOFCs.

The adoption of agricultural robots, or agrobots, has revolutionized modern farming operations, ranging from crop monitoring to automated harvesting, significantly boosting productivity. Motivated by the rapid advancements in agrobots and their integration into smart agricultural practices, this study proposes an autonomous trajectory tracking system for wheat farms using quadcopter UAVs. To address actuator fault detection, including stuck faults and partial loss of efficiency, a TSF-H∞-SMO (Takagi-Sugeno Fuzzy-based H∞ Sliding Mode Observer) fault detection framework is introduced. The approach initiates with the derivation of a TSF (Takagi-Sugeno Fuzzy) attitude control model that integrates an uncertainty term, constructed from the original nonlinear dynamics of the UAV and approximated through local linear models at four equilibrium positions. An actuator fault model is subsequently integrated to develop a comprehensive TSF-UAV model, accounting for actuator faults. The TSF-H∞-SMO is then designed using matrix coordinate transformation to enable precise fault detection. The fault detection capabilities of the TSF-H∞-SMO are evaluated through simulations on the TSF-UAV model under SISO (single-input single-output) actuator fault scenarios. The experimental results validate the proposed system, demonstrating its ability to detect a range of actuator faults accurately and promptly. The analysis reveals a proportional relationship between the amplitude of the state change and the severity of the fault, attributed to the interaction between system states and actuator flaps. This approach underscores the potential for deploying autonomous UAV-based fault detection and trajectory tracking systems in agricultural applications. Furthermore, integrating such advanced fault-tolerant control algorithms holds promise for consumer technology applications, where precision, reliability, and robustness are critical to enhancing system performance and operational efficiency.

Recent advances in deep learning for 3D point clouds have shown great promises in scene understanding tasks thanks to the introduction of convolution operators to consume 3D point clouds directly in a neural network. Point cloud data, however, could have arbitrary rotations, especially those acquired from 3D scanning. Recent works show that it is possible to design point cloud convolutions with rotation invariance property, but such methods generally do not perform as well as translation-invariant only convolution. We found that a key reason is that compared to point coordinates, rotation-invariant features consumed by point cloud convolution are not as distinctive. To address this problem, we propose a novel convolution operator that enhances feature distinction by integrating global context information from the input point cloud to the convolution. To this end, a globally weighted local reference frame is constructed in each point neighborhood in which the local point set is decomposed into bins. Anchor points are generated in each bin to represent global shape features. A convolution can then be performed to transform the points and anchor features into final rotation-invariant features. We conduct several experiments on point cloud classification, part segmentation, shape retrieval, and normals estimation to evaluate our convolution, which achieves state-of-the-art accuracy under challenging rotations.

Operating within the 300–500 °C range, low‐temperature solid oxide fuel cells (LT‐SOFCs) enable efficient and sustainable energy conversion, addressing the limitations of conventional high‐temperature SOFCs. However, achieving >0.1 S cm ⁻¹ ionic conductivity in electrolytes remains challenging. Here, a novel approach utilizing CuO self‐nucleation and electron‐ion (E‐I) coupling in x CuO/(1‐ x ) CeO ₂ (CCO) semiconductor ionic membranes ( x = 0–0.4) is presented. At the optimal 0.2CuO/0.8CeO ₂ composition, ionic conductivity exceeds 0.15 S cm ⁻¹ , driven by E‐I coupling at the CuO/CeO ₂ heterojunction. This coupling creates a built‐in electric field (BIEF) via interfacial charge transfer, facilitating ion transport by lowering the activation energy for ion migration. The dual‐conduction pathway enabled by E‐I coupling not only facilitates electronic transfer and ionic transport but also optimizes charge transfer kinetics, achieving exceptional power densities of 750–900 mW cm ⁻² at 500–550 °C and 78 mW cm ⁻² at 300 °C. Density functional theory (DFT) calculations further validate the role of Cu ²⁺ and Ce ⁴⁺ valence states in generating interfacial charge transfer and enhancing ionic mobility. This innovative approach positions CuO/CeO ₂ as a state‐of‐the‐art electrolyte, building the critical conductivity‐performance gap in LT‐SOFCs. This study pioneers LT‐SOFC innovation by leveraging E‐I coupling and electrode–electrolyte synergy, unlocking superior ion transport and practical applicability.