Automatic Anomaly Detection (AAD) has emerged as an advanced vision-based measurement method with diverse applications in healthcare and security. However, current AAD methods still face challenges related to data limitations and labeled-data imbalances, which limit the accuracy and reliability of AAD in real-life applications. Additionally, labeling and training large datasets for video anomaly detection (VAD) is computationally demanding and time-consuming. To address these challenges, this work introduces AADC-Net, a multimodal deep neural network for automated abnormal event detection and categorization. The key contributions of this research are as follows: 1) AADC-Net leverages pre-trained large language models (LLMs) and vision-language models (VLMs) to mitigate VAD dataset limitations and imbalances. 2) A pre-trained object detection model (DETR) is integrated for visual feature extraction, eliminating the need for bounding box supervision. 3) The experimental results demonstrate the state-of-the-art (SOTA) performance of the proposed AADC-Net with an Area Under the Curve (AUC) of 83.2% and an Average Precision (AP) of 83.8% on the public UCF-Crime and XD-Violence datasets, respectively. 4) Additionally, AADC-Net can be integrated into existing video surveillance systems, such as those in smart gyms and healthcare facilities, to automatically detect anomalies in real time with minimal supervision, enhancing security, monitoring, and reducing labor costs while minimizing human error. In summary, our results demonstrate that AADC-Net not only achieves high accuracy in anomaly detection but also provides a practical solution for real-world surveillance applications.

The objective of this study is to design a therapeutic method combining a portable high intensity focused ultrasound (HIFU) design which is suitable for the laboratory environment and a tailored integrated photo-acoustic imaging (PAI) system for monitoring thermal treatment. The electrical HIFU design is fabricated with changeable operating frequency and justified output power for resonating with different kinds of commercial transducers. The system’s control interface is built based on a touch screen to create a companionable interaction for users. The embedded fuzzy logic controller using the thermal input from the thermocouple sensor precisely drives the target temperature during HIFU exposure to achieve the expectedly coagulating results. The PAI system with 532-nm laser excitation is also integrated to define the affected region before and after HIFU treatment. The proposed fuzzy controller-integrated HIFU setup compatible with the PAI system is a feasible instrument in thermal therapy for ex vivo artificial tumors management.

Three-dimensional (3D) bioprinting has rapidly evolved into a controlling platform for the fabrication of patient-specific biomedical implants, with growing importance in advanced drug delivery systems. Beyond structural tissue engineering, bioprinted constructs now function as programmable therapeutic depots capable of localized, sustained, and stimuli-responsive drug release. This review focuses on recent biomaterial design strategies that enable precise control over drug encapsulation, retention, and release kinetics within 3D bioprinted architectures. The physicochemical and mechanical properties of bioinks, including crosslinking density, porosity, degradation behavior, viscoelasticity, and swelling characteristics, directly influence drug loading efficiency and release dynamics under physiological conditions. The rational tuning of these parameters allows the development of constructs that provide spatially controlled and temporally regulated therapeutic delivery. Recent advances in predictive modeling, such as finite element modeling (FEM), data-driven machine learning approaches, and ML, have significantly improved the ability to correlate material composition, printing parameters, and structural geometry with drug diffusion and degradation-mediated release mechanisms. These tools facilitate the optimization of printing variables including extrusion pressure, nozzle diameter, and layer resolution to ensure structural fidelity while maintaining therapeutic functionality. Emerging strategies incorporating multi-material printing, gradient architectures, and stimuli-responsive biomaterials have expanded the potential of 3D bioprinting for combination therapies and personalized medicine. This review discusses key challenges in translating bioprinted drug delivery systems into clinical applications, including the standardization of drug release characterization methods, and long-term stability assessment.

The precise identification and non-destructive measurement of structural features and defects in semiconductor wafers are essential for ensuring process integrity and sustaining high yield in advanced manufacturing environments. Unlike conventional measurement techniques, scanning acoustic microscopy (SAM) is an advanced method that provides detailed visualizations of both surface and internal wafer structures. However, in practical industrial applications, the scanning time and image quality of SAM significantly impact its overall performance and utility. Prolonged scanning durations can lead to production bottlenecks, while suboptimal image quality can compromise the accuracy of defect detection. To address these challenges, this study proposes LinearTGAN, an improved generative adversarial network (GAN)-based model specifically designed to improve the resolution of linear acoustic wafer images acquired by the breakthrough rotary scanning acoustic microscopy (R-SAM) system. Empirical evaluations demonstrate that the proposed model significantly outperforms conventional GAN-based approaches, achieving a Peak Signal-to-Noise Ratio (PSNR) of 29.479 dB, a Structural Similarity Index Measure (SSIM) of 0.874, a Learned Perceptual Image Patch Similarity (LPIPS) of 0.095, and a Fréchet Inception Distance (FID) of 0.445. To assess the measurement aspect of LinearTGAN, a lightweight defect segmentation module was integrated and tested on annotated wafer datasets. The super-resolved images produced by LinearTGAN significantly enhanced segmentation accuracy and improved the sensitivity of microcrack detection. Furthermore, the deployment of LinearTGAN within the R-SAM system yielded a 92% improvement in scanning performance for 12-inch wafers while simultaneously enhancing image fidelity. The integration of super-resolution techniques into R-SAM significantly advances the precision, robustness, and efficiency of non-destructive measurements, highlighting their potential to have a transformative impact in semiconductor metrology and quality assurance.

This study presents the design and implementation of a compact data acquisition system for immersive ultrasonic inspection of small-diameter pipelines, targeting applications where conventional systems are impractical due to size constraints. The system integrates the Eclipse Z7 platform with a customized pulser-receiver module and a rotary pipeline inspection gauge (PIG) equipped with a 5 MHz immersion-type ultrasonic transducer. The PIG module is designed to scan pipelines with an 8.18 mm wall thickness and a 200 mm inner diameter. Before deployment, real-time system calibration is performed via a connected computer interface to ensure optimal performance. Once inside the pipeline, the PIG operates autonomously, with ultrasonic data being acquired and stored locally on a Raspberry Pi. Post-inspection, the recorded data is extracted and analyzed on the computer to assess pipeline integrity. The proposed system offers a compact alternative to commercial solutions, particularly in scenarios involving limited access and small-diameter pipelines.

Ultrasonic anemometers are widely used for wind measurement owing to their fast response, high temporal resolution, and long operational lifetime with minimal recalibration requirements. However, most configurations suffer from transducer shadowing, where cylindrical probes disrupt local airflow and introduce systematic errors in time-of-flight (TOF) measurements. While prior computational fluid dynamics (CFD)-based investigations have characterized this effect, their analyses remain confined to low wind speeds, and no existing study has explicitly proposed a method to mitigate shadowing-induced bias. This paper presents a coupled CFD and acoustic propagation framework to analyze wake-induced velocity deficits and their effects on TOF measurements for a three-transducer ultrasonic anemometer, with simulations spanning 5 to 75 m/s over the full 360° range. The results show that shadowing distortions are strongly direction-dependent, peaked within approximately ±5° angular sectors, with a near-constant velocity deficit of approximately 40% along affected paths. A shadowing-aware acoustic path selection method is then proposed that selectively excludes corrupted acoustic paths, reducing the average velocity root-mean-square error (RMSE) to 0.349 m/s and the directional RMSE to 1.14°, representing improvements of more than an order of magnitude over shadow-unaware methods. These findings provide a physically grounded, simulation-based framework for shadowing-aware wind measurement using ultrasonic anemometers.