Jilin Province Development and Reform Commission

As a mainstream form of 3D data, point clouds are widely used in computer vision for tasks such as segmentation, classification, and target detection due to their simple representation method and high stability and accuracy. Considering the issues of noise points and uneven point distribution in current generation models, we propose a novel adversarial network model, SAC-GAN, which incorporates both a self-attention mechanism and a curvature learning mechanism. Firstly, the feature enhancement module and the pre-processing module, which are based on the ShapeNetCore open dataset, are designed on the generator to enhance the authenticity of local geometric details in the generated point cloud. Secondly, the loss function of the discriminator is adjusted to combine the traditional Wasserstein distance with the normal vector of key points to guide the generation of subtle features of point clouds and improve the quality and consistency of generated point clouds; Finally, to enhance the discriminator's capacity to extract both local and global features, a self-attention mechanism is introduced. This enhances the discriminator's capacity to discern the details of the generated point cloud and offers superior feedback to the generator. Experimental results indicate that the proposed point cloud generation model outperforms existing methods, including TreeGAN, SP-GAN, PDGN, and WarpingGAN, in terms of generation quality. Specifically, the model achieves a reduction in JSD by 4.24, a decrease in MMD by 0.8, and an increase in COV by 1.25%. It can be proven that the point cloud generated by SAC-GAN model has a good performance in shape integrity and authenticity.

As the strategic position of the oceans in the political, military, and economic fields is increasing and the mankind’s use of ocean space is constantly being strengthened, underwater unmanned equipment is of vital importance and embraces a wide range of prospects for application, which plays a vital role in modern maritime military strategy, and is a key factor in ensuring that an advantage is gained in the field of underwater warfare in the new era. Underwater unmanned equipment can perform tasks such as environmental monitoring, target detection, anti-submarine, and anti-mine warfare, and can also be used as a weapon platform and a support platform, with the characteristics of strong concealment and low cost, and can be fitted with an optional payload according to the type of mission. As an essential part of underwater unmanned equipment, depth sensor is responsible for completing the deployment depth of the equipment in the seawater or sailing depth measurement, and the seawater pressure signal is converted into an electrical signal sent to the fuzzing device or control system and other electrical components for decoding and calculation, in order to control the sailing depth, thus completing the measurement of the automatic adaptation range in different depths, so as to ensure that the accuracy and the effectiveness of the target to be measured, which performs a crucial function in the process of equipment action. Meanwhile, for some underwater equipment, especially weaponry, the internal space is compact, the structure is complex, and the integration is high, making disassembly extremely difficult. Long-term use and storage, for sensors, cause time drift, which requires recalibrating. However, when sensors undergo time drift during long-term use or storage and require recalibration, they can only be removed and sent back to the manufacturer for maintenance during major overhauls. This process is time-consuming, and the secondary assembly after disassembly poses safety risks, significantly impacting the equipment’s performance. This paper introduces a CAN bus depth sensor with the capability of online calibration without disassembly, enabling the product to be quickly calibrated online without returning to the factory. This enhances the equipment’s supportability and ensures its long-term stable operation.

This paper aims to study how to efficiently achieve large-area lidar mapping of intertidal zones and more accurately reflect their topographic structure. It employs a terrain module classification and modeling method to classify and reorganize terrain structures such as woodland, plains, and hills. The BP neural network toolbox in MATLAB software is used to call mathematical models to train and learn the above various terrain modules, which are then applied to the overall point cloud data modeling analysis of Dongshan Island. Through field research and comparative analysis with remote sensing mini-plan views, it is concluded that using the terrain module classification and modeling method for laser point cloud modeling can better reflect the structural changes and topographic relief characteristics of complex intertidal zone terrains.

BackgroundPharmaceutical companies continuously pursue healthcare professionals, starting from the medical college level, which can ultimately lead to irrational prescribing of drugs and antibiotics. Therefore, our main aim was to evaluate the opinions and attitudes of medical students toward pharmaceutical promotion.MethodsThis study utilized a cross-sectional online survey that applied the snowball sampling technique. Data were collected from three public and three private sector medical colleges in Punjab, Pakistan using snowball sampling. A modified version of a pre-structured questionnaire was used to collect data between October 2020 and January 2021. Medical students from the third year onward were captivated. The tool was made available on Google Forms and students could access it by clicking the link shared. The effect of promotion on prescribing pattern and future prescribing of antibiotics were measured. Descriptive statistics, chi-square, and t-test were used to analyze the data.ResultsA total of 1,301 students filled out the survey, but only 1,227 responses were acceptable. The average age was found to be 23.4 ± 1.59 years. Slightly more than half of the respondents were male participants (57.7%), and a significant proportion (84.1%) reported being aware of pharmaceutical promotion. A smaller number (27.7%) felt that physicians who meet medical representatives more frequently tend to prescribe more antibiotics and 46.3% indicated they would be willing to prescribe antibiotics under the promotional influence. Medical students who were male, in senior college years, attended government institutions, and had lower parental income showed significantly higher perception and attitude scores (p < 0.05) which, in turn, may show their inclination to promotional activities. Many students agreed with the view that pharmaceutical promotion (PP) activities may alter prescribing practices and also believed that they contribute to the increased irrational prescribing of drugs and antibiotics.ConclusionThe study revealed that only a small number of students are willing to engage in promotional activities and accept rewards, which influences their choice toward selection of drugs and antibiotics. This study highlighted the necessity of giving proper educational instructions regarding the promotion of drugs to medical students. This study also focused on the educational prerequisites of the students.

Water conservancy project is the most basic livelihood guarantee project related to social security and stability and the survival and development of various production departments, but it is facing huge financial pressure because of its large investment scale, low financial returns, long recovery cycle, positive external benefits and strong public welfare. This paper analyses the characteristics and difficulties of investment and financing of water conservancy projects, combined with the new concept of the development of water conservancy projects and the analysis of related cases, this paper puts forward some solutions to investment and financing problems, such as innovation of investment and financing mode, establishment of local water conservancy development platform company, invigorating stock assets, etc., as well as relevant policy suggestions such as overall project planning, improving investment mechanism, improving financing capacity, expanding investment returns, innovating investment and financing models, etc. It can provide reference for the overall planning and investment construction of water conservancy projects.

The job shop scheduling problem is an abstraction of the job processing process in the manufacturing workshop, which has important economic value in real life. At present, most of the research on job shop scheduling problems based on reinforcement learning is based on a single agent architecture, which makes the number of interactions required in the solving process directly related to the size of the problem. There are difficulties in training the model on large-scale instances. Propose an end-to-end method based on multi-agent reinforcement learning, treating each machine as an agent and making processing decisions for multiple machines in one interaction, while training problem scale independent strategies. Using disjunctive graphs as a representation of states in problem modeling to model the execution order relationships between operations. In network design, a graph neural network based on induced point attention is further proposed for feature extraction to overcome the problems of high computational complexity and attention degradation. The experiments on the Taillard dataset show that the performance of our model is superior to multiple traditional methods and a recent single agent reinforcement learning method.

BackgroundPositive surgical margin (PSM) or apical positive surgical margin (APSM) is an established predictive factor of biochemical recurrence or disease progression in prostate cancer (PCa) patients after radical prostatectomy. Since there are limited usable magnetic resonance imaging (MRI)-based models, we sought to explore the role of three-dimensional (3D) visualization for preoperative MRI in the prediction of PSM or APSM.MethodsFrom December 2016 to April 2022, 149 consecutive PCa patients who underwent radical prostatectomy were retrospectively selected from the Second Affiliated Hospital of Dalian Medical University. According to the presence of PSM or APSM, patients were divided into a PSM group (n=41) and a without PSM group (n=108) and into an APSM group (n=33) and a without APSM group (n=116). Twenty-one parameters, including prostate apical shape, PCa distance to the membranous urethra, and pubic angle, were measured on 3D visualization of MRI. The development of the nomogram models was built by the findings of multivariate logistic regression analysis for significant factors.ResultsTo predict the probability of PSM, a longer PCa distance to the membranous urethra (OR=0.136, p=0.019) and the distance from the anterior peritoneum to the anterior border of the coccyx (work space AP, OR=0.240, p=0.030) were independent protective factors, while a type 3 prostate apical shape (OR=8.262, p=0.025) and larger pubic angle 2 (OR=5.303, p=0.029) were identified as independent risk factors. The nomogram model presented an area under the curve (AUC) of the receiver operating characteristic curve (ROC) of PSM of 0.777. In evaluating the incidence of APSM, we found that the distance to the membranous urethra (OR=0.135, p=0.014) was associated with a low risk of APSM, while larger pubic angle 1 (OR=4.666, p=0.043) was connected to a higher risk of APSM. The nomogram model showed that the AUC of APSM was 0.755.ConclusionAs 3D visualization for preoperative MRI showed good performance in predicting PSM or APSM, the tool might be potentially valuable, which also needs to be validated by multicenter, large-scale, prospective studies.

The gas temperature, electron temperature, and electron density are critical parameters that affect microwave plasma chemistry processes in industrial applications. Using optical emission spectroscopy (OES), the gas temperature and electron density of a 915-MHz atmospheric pressure air microwave plasma torch (MPT) were investigated at various absorbed power levels and radial positions. In addition, the variation in the electron temperatures of 915-MHz atmospheric pressure argon microwave plasma filaments was analyzed as a function of the absorbed power. The experimental results showed that: 1) the absorbed power had little effect on the radial gas temperature or electron density; 2) the skin effect was present in the 915-MHz atmospheric pressure air MPT, where it causing caused hollowing in the center of the radial gas temperature distribution; 3) the electron density reached approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times10$ </tex-math></inline-formula> 13 cm−3 and was almost insensitive to variations in the absorbed power in the center of the 915-MHz atmospheric pressure air MPT; and 4) the upper limit of the 915-MHz atmospheric pressure microwave plasma filament electron temperature increased from approximately 1 to approximately 3 eV as the absorbed power increased from 2.0 to 6.3 kW.