Abstract:As a vital part of traditional Chinese medicine, acupuncture has broad global applications. However, the reliance on practitioners'' experience for acupoint localization in traditional acupuncture methods leads to a lack of standardization, restricting its reproducibility and broader adoption. Acupuncture robots, as intelligent medical devices, offer new opportunities for standardizing and promoting acupuncture techniques. This paper introduces an improved YOLOv8-Pose model, YOLO-PointMap, designed to address challenges in dense acupoint distribution and weak feature recognition. By incorporating dynamic convolution to optimize the C2f module and introducing a channel-attention-based feature fusion module, the model achieves significant advancements in multi-scale feature extraction and integration. Experimental results show that the EPE, PCK and mAP50-95 (Pose) indexes of YOLO-PointMap on the test set are superior to the existing methods, with the values reaching 3.27, 1 and 84.9% respectively, especially in dense key point identification and weak feature region localization. It provides strong support for the development of acupuncture robot technology, and shows the potential application value in the fields of virtual reality and intelligent interaction.

Abstract:Flexible robotic endoscopy have been employed in minimally invasive surgeries, however the non-linear deformable characteristics of the flexible endoscope complicate its control. This paper proposes an optimal teleoperation control method for flexible robotic endoscopy using neurodynamic optimization. A master-slave motion mapping strategy is designed in image space and the kinematic model of the flexible endoscope is established based on the constant curvature assumption. It obtains the mapping relationship between image feature velocity and actuation velocity. Considering robot physical constraints, an optimal control problem based on quadratic programming (QP) is constructed. A neurodynamic optimization method is designed to solve the complex QP problem. Experiments were conducted on a ureteroscope robot system to validate the effectiveness of the method. Experimental results indicate that the proposed method can significantly reduce human control errors and vibrations, maintaining target point tracking precision within 2.5%. The feasibility of the proposed method in ureteroscopic lithotripsy was also verified.

Abstract:BACKGROUND:For cone beam computed tomography (CBCT), there has long been a wish to modulate the intensity and distribution of the X-rays to accommodate the patient""s anatomy as the gantry rotates from one projection to another. By doing so, both image artifacts and radiation dose would be reduced. However, the current beam modulation setups, such as dynamic bowtie filters, may be too complex for practical use in clinical applications. OBJECTIVE: This study aimed to investigate a simplified dynamic beam filtration strategy for CBCT imaging to reduce image artifacts and radiation dose. METHODS: In this study, the beam filtration was designed to vary dynamically as the gantry of the CBCT rotates around the object. Specifically, two distinct parts were integrated together: the sheet filter part and the bowtie filter part. The dynamic beam filtration setup has two working schemes, one is a combination of dynamic sheet filter and dynamic bowtie filter, denoted as dynamic filter-dynamic bowtie (DFDB); the other is a combination of dynamic sheet filter and static bowtie filter, denoted as dynamic filter-static bowtie (DFSB). Numerical imaging experiments were performed with respect to three human body parts: shoulder, chest, and knee. In addition, the Monte Carlo simulation platform MC-GPU was used to generate the dose distribution maps. RESULTS: Results showed that the use of the proposed DFDB and DFSB beam filtration schemes can significantly reduce the image artifacts and thus improve the CBCT image quality. Depending on the scanned object, the total radiation dose may be reduced by 30%. CONCLUSIONS: The proposed simple dynamic beam filtration strategy, especially the DFSB approach, might be helpful in the future to improve the CBCT image quality with reduced image artifacts and radiation dose.

Abstract:Electromagnetic pulse sound source (Boomer) is a commonly used explosion sound source in marine seismic exploration, and the deep-sea application of such explosion sound source needs to solve cavitation suppression problem. In this paper, a deep-sea boomer source based on pressure compensation balance is proposed. A boomer transducer with a maximum working pressure of 20MPa is developed and tested in a high-pressure anechoic tank. Through the analysis of the hydrophone outputs under different energy and pressure levels, it can be seen that an air sac with the initial pressure of 0.5MPa can effectively balance the internal and external pressure of the transducer, solve the problem of cavitation suppression, and realize the excitation of broadband pulse sound waves. The repeatability of the acoustic wave is very good, and the minimum correlation coefficient is to 0.986. With the increase of working pressure from 0.5MPa to 20MPa, the main change in acoustic characteristics is the amplitude attenuation (204.6dB to 194.2 dB) and width compression (182μs to 88μs), and the main frequency (2.3kHz as the center) slightly shifted to high frequency. Compared with the hydrophone output in the process of pressure rising and downing in the high-pressure anechoic tank, it can be seen that the repeatability of the acoustic wave is better. The higher the pressure, the better the waveform consistency, indicating that the boomer transducer based on pressure compensation balance has a more stable performance under high pressure environment.

Abstract:This article introduces a modular design concept that can realize the general observation of marine environment and multi -equipment integration of the universal marine buoyant data collection system. This system uses ARM chip timer and interrupt controller to virtually collects multi -module parallel collection processing circuits. The full system is divided into three modules: meteorological security, hydrotoma and communication according to the function of marine data buoyoma functions, and realizes the continuous collection and processing of buoyant multi -equipment, as well as real -time two -way communication. The serial extension chip realizes the system interface expansion, which improves the system"s installation capacity from the hardware. The communication module uses DMA technology to realize the functions of real -time data of dual -road real -time data, which realizes the reliable and safe operation of the buoyant system on the sea, and also improves its human -computer interaction function. This system has verified the stability, reliability, and measurement accuracy of the system through laboratory testing and operation experiments at sea.

Abstract:Chiplet-based multi-chip integration designs provide a flexible and scalable solution that surpasses traditional SoC (System on Chip) monolithic integration. However, inter-chiplet communication has become a significant bottleneck affecting overall system performance. The Network on Interposer (NoI) plays a pivotal role in multi-chip systems, directly influencing both performance and development costs. In this paper, we review NoI communication topologies for heterogeneous chiplets. We thoroughly explore the importance of current inter-chiplet communication architectures and discuss their design and implementation methods. This paper covers the entire communication process, spanning from protocol and interface layers to the application layer, classifying interconnect topologies based on their structural configurations and providing in-depth analyses and cross-comparisons for each category. Furthermore, we investigate future directions in NoI communication technologies, identifying technical challenges and potential solutions. We also propose advanced evaluation methods and modeling techniques for reusable interposer layers and topologies. This review aims to provide researchers with a thorough understanding of the current landscape and future trends in NoI technology, emphasizing its crucial role in advancing next-generation semiconductor devices across a wide spectrum of applications.

Abstract:Ketamine, an N-methyl-D-aspartate receptor (NMDAR) antagonist, is clinically utilized for sedation, anesthesia, and the treatment of refractory depression. However, its addictive properties restrict its clinical application. A dose of 0.5 mg/kg is commonly used as an antidepressant in clinical settings, while 15 mg/kg represents the dose typically abused. The effects of varying doses of ketamine on brain network activation remain unclear. In this experiment, two representative doses of ketamine, 0.5 mg/kg and 15 mg/kg, were administered via intraperitoneal injection for 7 consecutive days. Brain network activation was assessed by examining the expression of the immediate early gene protein (cFos). Results indicated that, compared to the saline control group, 0.5 mg/kg ketamine significantly increased the number of cFos-positive cells in the medial prefrontal cortex, intermediate lateral septal nucleus, and periaqueductal gray matter. Conversely, 15 mg/kg ketamine significantly increased cFos expression in the nucleus accumbens, lateral habenula, hippocampal CA3 region, amygdala, and ventral tegmental area. These findings suggest that ketamine"s activation of brain networks is dose-dependent, with different doses activating distinct brain regions. This study lays a foundation for investigating the neuropharmacological effects of different ketamine doses and provides a reference for identifying brain regions associated with its antidepressant and addictive properties.

Abstract:The usage of mmWave radar for non-contact vital signs monitoring has shown great potentials in the medical and healthcare fields, which enables continuous and imperceptible identity verification. Due to the complex impact of various factors on heart movement, the FMCW mmWave radar can better monitor and capture heart data during sleep, and the obtained heart data can be recognized and classified based on the uniqueness of personal heart movement characteristics. In this study, we propose a deep convolution neural network for identification recognition from one-dimensional time series data of the heart radar signal. The results were compared with 3 SOTA methods, i.e. LSTM, InceptionTime and LSTformer. All the models achieved classification accuracies about 90% on an experimentally acquired heart signal data set in sleep posture. The InceptionTime model has the highest accuracy, but it takes the longest time. The LSTM and LSTformer models have the lower accuracy but the shorter calculation time. The proposed CNN model can obtain similar accuracy but better efficiency in comparison with InceptionTime model.

Abstract:LOGO is a new distributed computing framework using a Non-MapReduce computing paradigm. Under the LOGO framework, big data distributed computing is completed in two steps. The LO operation runs a serial algorithm in a number of nodes or virtual machines to process independently the random sample data blocks, generating local results. The GO operation uploads all local results to the master node and integrate them to obtain the approximate result of the big data set. The LOGO computing framework eliminates data communication between nodes during iterations of the algorithm, greatly improving computing efficiency, reducing memory requirements, and enhancing data scalability. This article proposes a new distributed machine learning algorithm library RSP-LOGOML under the LOGO computing framework. A new distributed computing is divided into two parts: the serial algorithm executed by the LO operation and the ensemble algorithm executed in the GO operation. The LO operation can directly execute existing serial machine learning algorithms without the need to rewrite them according to MapReduce. The GO operation executes ensemble algorithms of different kinds depending on the ensemble tasks. In this article, the principle of LOGO distributed computing is introduced first, followed by the algorithm library structure, the method for packaging existing serial algorithms and the ensemble strategy. Finally, implementation in Spark, App development, and the results of performance tests for various algorithms are demonstrated.

Abstract:The ability to mimic the microenvironment of the human body through fabrication of scaffolds itself a great achievement in the biomedical field. However, the search for the ideal scaffold is still in its infant stage and there are significant challenges to overcome. In the modern era, scientific communities are more attracted to natural substances due to their excess biological ability, low cost, biodegradability, and lesser toxic than synthetic lab made products. Chitosan is a well-known polysaccharide that has recently grabbed high amount of attention for its biological activities, especially in 3D bone tissue engineering (BTE). Chitosan greatly matches with the native tissues and thus stands out as a popular candidate for bioprinting. This review focuses on the potential of chitosan based scaffolds advancement and the drawbacks in bone treatment. Chitosan-based nanocomposites have exhibited strong mechanical strength, water-trapping ability, cellular interaction, and biodegradability characteristics. Chitosan derivatives have also encouraged and provided different routes of treatment and enhanced biological activities. 3D tailored bioprinting have opened new doors to design and manufacture scaffolds of biological, mechanical, and topographical properties.