Hydraulic fracturing is a significant technique in petroleum engineering to enhance the production of shale gas or shale oil reservoir. The process of hydraulic fracturing is extremely complicated, referring to the deformation of solid formation, fluid flowing in the crack channel, and coupling the solid with fluid. Simulation of hydraulic fracturing and understanding the course of the mechanism is still a challenging task. In this study, two hydraulic fracturing models, including the Khristianovic–Geertsma–de Klerk (KGD) problem and the hydraulic fracture (HF) intersection with the natural fracture (NF), based on the zero thickness pore pressure cohesive zone (PPCZ) element with contact friction is established. The element can be embedded into the edges of other elements to simulate the fracture initiation and propagation. However, the mesh type of the elements near the PPCZ element has influences on the accuracy and propagation profile. Three common types of mesh, triangle mesh, quadrangle mesh, and deformed quadrangle mesh, are all investigated in this paper. In addition, the infinite boundary condition (IBC) is also discussed in these models. Simulation indicates that the results of pore pressure for zero toughness regime simulated by the triangle mesh are much lower than any others at the early injection time. Secondly, the problem of hydraulic fracturing should be better used with the infinite boundary element (IBE). Moreover, suggestions for crack intersection on the proper mesh type are also given. The conclusions included in this article can be beneficial to research further naturally fractured reservoirs.

The knowledge of cracking mechanisms is significant for evaluating the healthy condition of aircraft structures and can be retrieved by moment tensor inversion based on the acoustic emission (AE) phenomenon. For engineering applications, the inversion method cannot compute accurate results because the waveforms recorded by sensors are commonly contaminated by noise. Consequently, the correlation calculation of de-noising is introduced into the inversion and sufficient correlation functions are needed. In this paper, the correlation function of raw waveforms is proposed and based on the inherent similarity between the signals induced by one source and recorded by different sensors. According to the synthetic tests, the error of the inversion method based on the new correlation function is approximately 1/10 of that of the commonly used amplitude method. Although the inversion accuracy is influenced by the phase differences and the ratio of noise frequency to signal frequency, the influence is limited and the new correlation function is suitable for most practical cases. The inversion method based on the new correlation function does not require the knowledge of noise spectra or any complex calculation processes and provides a new way to improve the inversion accuracy of cracking mechanisms with little additional computation consumption.

Position-Based Visual Servoing (PBVS) and Image-Based Visual Servoing (IBVS) struggle to balance end effector pose accuracy and robustness in apple picking. They are also prone to target loss and control singularities. A progressive Hybrid Automatic Switching Visual Servoing (HAVS) method is proposed and applied to an apple-picking robotic system. HAVS integrates PBVS and IBVS to coordinate control of the manipulator end effector pose. A depth-based switching function is designed. When target depth is below an optimal threshold, the controller switches to PBVS for precise final positioning. This reduces target loss and control singularities. An adaptive proportional-derivative (PD) controller with fuzzy gain scheduling updates the control gains online to enhance responsiveness and stability. The hardware consists of a six-axis manipulator, a depth camera, and a mobile base. You Only Look Once version 5 (YOLOv5) performs apple detection and generates control commands. Indoors, success rate was 96%, which was 4 and 10 percentage points higher than PBVS only and IBVS only. Average picking time was 12.5 s, 0.3 s, and 1.1 s shorter. Outdoors, success rate was 87.5%, average time was 13.2 s, and damage rate was 4.2%. This method provides a reference implementation for visual servo control in agricultural picking robots.

An integrated QR-based identification and multi-participant quiz system is developed for classroom and competition scenarios. It reduces the check-in latency, removes fixed buzz-in timing, and lifts hardware-imposed limits on the participant count. On the software side, a MATLAB-R2022b-based module integrates the generation and recognition of linear barcodes and QR Codes, enabling fast, accurate acquisition of contestant information while reducing the latency and error risk of manual entry. On the hardware side, control circuits for compulsory and buzz-in modules are designed and simulated in Multisim-14.3. To accommodate diverse scenarios, the team-versus-team buzz-in mode is extended to support two- or three-member teams. Functional tests demonstrate the stable display of key states—including contestant identity, buzz-in priority group ID, and response duration. Compared with typical MCU-channel-based designs, the proposed system relaxes hardware-channel constraints, decoupling the participant count from fixed input channels. It also overcomes fixed-timing limitations by supporting scenario-dependent configuration. The Participant Information Registration subsystem achieved a mean accuracy of 86.7% and a mean per-sample computation time of 14 ms. The 0–99 s configurable timing aligns with question difficulty and instructional procedures. It enhances fairness, adaptability, and usability in formative assessments and competition-based learning.

Prolonged sitting and poor posture are linked to musculoskeletal discomfort, higher spinal loading, and lower study and work efficiency. An Arduino-based distributed system combining a multi-sensor was developed for low-cost, camera-free sitting posture monitoring. It comprises a wearable sensing board (WSB), a main control board (MCB), and a host computer. The WSB measures trunk inclination—that is, the forward pitch and lateral roll of the upper trunk relative to the upright reference—using an ADXL345 acceleration sensor, whereas the MCB measures the user-to-desk distance using a US-100 ultrasonic ranging unit; NRF24L01 Wireless Communication Units connect them. Rule-based thresholds classify six states: “normal”, “slouching”, “leaning left”, “leaning right”, “too close”, and “too far”. The Sound Audio Unit and Liquid Crystal Display Unit provide local voice alerts and visual feedback. Using a 4G Unit, the MCB uploads user ID, timestamp, ambient temperature, distance, and posture state to a cloud platform. Cloud-generated text files support host retrieval and display, with accounts for two users and one administrator. The system can determine sitting-distance states within a range of 40–2000 mm and output trunk inclination information over a range of 0–90°. Under the current test conditions, the wireless communication distance between the MCB and WSB exceeds 3 m. In addition, the auditory reminder, time and temperature display, and PC-side data retrieval functions all operate as intended. With a total hardware cost of USD 18.39, the system provides a viable prototype for low-cost, camera-free sitting posture monitoring and remote data management in educational and home settings.