bluuebunny/crossref_metadata_2025 huggingface dataset:
Uncertain data density peak clustering algorithm based on JS divergence
Authors: Song Li , Xiaonan Liu , Juan Liu
Published: October 2024

Aiming at the defects of traditional density-based uncertainty clustering algorithms, such as parameter sensitivity and poor clustering results for complex manifold uncertain data sets, a new uncertainty data density peak clustering algorithm based on JS divergence (UDPC-JS) is proposed. The algorithm first removes noise points by using the uncertain natural neighbor density factor defined by the uncertain natural neighbor. Secondly, the local density of uncertain data objects is calculated by combining the uncertain natural neighbor and JS divergence. The initial clustering center of the uncertain data set is found by combining the idea of representative points, and the distance between the initial clustering centers is defined based on JS divergence and graph. Then, the decision graph is constructed on the initial clustering center using the local density calculated based on the uncertain natural neighbor and JS divergence and the newly defined distance between the initial clustering centers based on JS divergence and graph, and the final clustering center is selected according to the decision graph. Finally, the unassigned uncertain data objects are assigned to the cluster where their initial clustering center is located. Experimental results show that the algorithm has better clustering effect and accuracy than the comparison algorithm, and has a greater advantage in processing complex manifold uncertain data sets.
Traffic speed prediction of regional complex road network based on CapsNet and D-BiLSTM
Authors: Jie Cao , Guang Su , Kock Imke de , Penghui Li
Published: October 2024

Accurate and efficient short-term traffic prediction is of great significance in the study of regional traffic network. However, the complex and dynamic spatiotemporal correlation of traffic patterns makes the existing methods insufficient in learning traffic evolution in terms of structural depth and prediction scale. Therefore, this paper proposes a deep learning model combining capsule network (CapsNet) and deep bidirectional LSTM (D-BiLSTM). CapsNet is used to identify the spatial topological structure of the road network and extract spatial features, and D-BiLSTM network is integrated. The forward and backward dependencies of traffic states are considered at the same time, and the bidirectional temporal correlation of different historical periods is captured to predict the traffic of large-scale complex road networks in the target area. Experiments on real traffic network speed datasets show that the prediction accuracy of the proposed model is improved by more than 10% on average , which is better than other methods. It has high prediction accuracy and good robustness in traffic prediction of regional complex road networks.
Prediction of rotational bending fatigue life of bearing steel based on damage mechanics
Authors: Yibing Yu , Ziqiang He , Xiaofang He , Zhenyu Yang , Zhixin Zhan
Published: October 2024

Based on the theory of continuous damage mechanics, this paper proposes a fatigue damage evolution model and numerical calculation method considering the influence of vacuum chemical heat treatment process, and studies the rotational bending fatigue damage analysis and life prediction method of M50NiL bearing steel. Firstly, the constitutive model, fatigue damage evolution equation and material parameter calibration method in the theoretical model are given. Secondly, based on the ABAQUS platform, the damage mechanics-finite element numerical calculation method of fatigue damage analysis considering the influence of hardened layer is realized by writing the UMAT subroutine; then, the rotational bending fatigue test of M50NiL bearing steel treated by vacuum chemical heat treatment of quenching and tempering, carburizing and carbonitriding is carried out, and the influence of heat treatment process on fatigue performance is analyzed; finally, based on the proposed fatigue damage model and numerical calculation method, the rotational bending fatigue life of M50NiL bearing steel is predicted, and the results are compared with the experimental results to verify the correctness of the proposed method.
Fault diagnosis of bearings under variable operating conditions based on domain adaptation
Authors: Jie Cao , Haonan Yin , Xiaogang Lei , Jinhua Wang
Published: October 2024

Aiming at the problem of low fault recognition rate caused by different distribution of training samples and test samples and imbalance of various fault data in bearing fault diagnosis, a domain adaptive fault diagnosis method based on improved residual network ( ResNet ) is designed. In the first layer of the diagnosis network, a multi-dimensional convolution structure is used for feature extraction to obtain fault feature information of different dimensions; the local maximum mean difference (LMMD) is used in the domain adaptive layer to align the distribution of the source domain and the target domain to obtain more fine-grained information; the class balance loss function ( CBLoss ) is used to solve the training problem of unbalanced data, and the Adam optimization network is used to realize fault diagnosis. Experimental results show that the proposed improved method can achieve higher diagnosis results under the imbalance of fault sample categories. Experimental verification is carried out on two bearing data sets and collected wind turbine data. The results show that the proposed improved method has certain advantages. The diagnostic performance of the proposed improved method in the case of unbalanced data samples is better than other deep transfer learning methods, and it can be used as an effective cross-condition fault analysis method.
Game model of high-speed railway delay and passenger choice behavior under incomplete information
Authors: Aiguo Lei , Qizhou Hu , Xiaoyu Wu , Siyuan Qu
Published: October 2024

The arrival status of high-speed trains is an important factor affecting the choice behavior of railway passengers. In order to specifically analyze the influence of the arrival state of high-speed trains on the choice behavior of railway passengers, evolutionary game theory was used to analyze the arrival state of high-speed trains and the choice behavior of railway passengers, and the game matrix of the arrival state of high-speed trains and the choice behavior of railway passengers was constructed. The location of the equilibrium point and the system evolution mechanism are deeply analyzed. Matlab simulation results show that the evolutionary game behavior finally has two evolutionary stable states. When high-speed trains delay time is less than 30 min , the train tends to arrive on time, and the railway passengers tend to choose the ride strategy. When the delay time is greater than 30min, the train tends to arrive on time, and the railway passengers tend to choose the refund strategy. By improving the facilities in the high-speed railway station, the railway passengers can choose the transfer strategy. Changing the value of the game parameters can adjust the evolution direction of both sides of the game and effectively improve the proportion of railway passengers when the high-speed train is delayed.
A method for extracting Martian terrain features based on unsupervised contrastive learning
Authors: Bo Yang , Xiang Wei , He Yu , Chaofang Liu
Published: October 2024

Intelligent recognition of Martian surface terrain is of great significance to autonomous exploration of Mars rovers. Feature extraction methods for Martian terrain images are currently mainly divided into two categories: traditional shallow visual feature extraction and deep feature extraction based on supervised learning. Loss of image information and the need for a large amount of labeled data are key issues to be addressed. A method for Martian terrain feature recognition based on unsupervised contrastive learning is proposed. By establishing an image dictionary dataset, two sets of neural networks, "query" and "encoding", are used to compare a single image with other images in the "dictionary" dataset, and the network is trained using the similarity functional as the loss function, thereby realizing feature recognition of Martian terrain images. The proposed method also has the ability to recognize new types of terrain images outside the training dataset, and has outstanding superiority in subsequent recognition and classification. Computer simulation verification shows that the recognition accuracy of the proposed method is and the 85.4%recognition accuracy of new types of terrain images is 84.5%.
Research on charging scheduling optimization of pure electric buses considering peak-valley time-of-use electricity prices and battery loss costs
Authors: Feifeng Zheng , Zhixin Wang , Ming Liu
Published: October 2024

In recent years, with the accelerated progress of pure electric buses in my country, the charging scheduling and operation plan of pure electric buses urgently needs to be rationalized and scientific. This study takes the charging scheduling operation in the daily operation of pure electric buses as the starting point, and effectively describes the battery loss cost function by analyzing the optimal fluctuation range of the state of charge of the power battery of pure electric buses. Combined with the analysis of the characteristics of peak and valley time-of-use electricity prices during the charging period, the problem is refined into a pure electric bus charging scheduling decision with controllable operation completion, and the parallel machine scheduling theory is used to characterize and solve the mathematical optimization model, with the aim of minimizing the operating cost of bus companies. At the same time, an immune optimization algorithm based on random key encoding and a greedy algorithm based on the idea of avoiding peaks and filling valleys are developed to solve large-scale problems. In addition, the effectiveness of the algorithm and the applicability of the method are verified through the real bus network in Shanghai.
Intelligent prediction of surface roughness of PSZ ceramic grinding based on correlation analysis and CNN-BiLSTM neural network
Authors: Li Guo , Liangrui Zheng , Lang Feng
Published: October 2024

Partially stabilized zirconia (PSZ) ceramics are widely used in aerospace industry and other fields due to their superior performance. Surface roughness is a key indicator for evaluating the grinding level of PSZ ceramics. In order to reduce the prediction error of grinding surface roughness, an acoustic emission prediction model for PSZ ceramic grinding surface roughness based on correlation analysis and convolution-bidirectional long short term memory neural network (CNN-BiLSTM) was proposed. By analyzing the correlation between the eigenvalues of grinding acoustic emission signals and the grinding surface roughness values, the most relevant frequency bands and feature matrices between the grinding acoustic emission signals and the grinding surface roughness were selected as the input parameters of the CNN-BiLSTM neural network to reduce the error of acoustic emission prediction of grinding surface roughness. The research results show that the average prediction error of PSZ ceramic grinding surface roughness based on correlation analysis and CNN-BiLSTM neural network is less than 3.92%.
K-means-ten clustering algorithm based on min-max criterion and region division
Authors: Fuheng Qu , Jian Song , Yong Yang , Yating Hu , Yuetao Pan
Published: October 2024

Aiming at the problem of unstable clustering results and low solution accuracy of -means- + algorithm, an I--means-+ clustering algorithm based on min-max criterion and region division is proposed. Firstly, the min-max criterion is proposed to calculate the distance from each data point to the nearest center, and the data point with the largest distance is preferentially selected as the new cluster center to avoid the situation where multiple initial centers are clustered in the same cluster; secondly, the data points in the split cluster are divided into different regions, and a data point is selected in each region as the candidate center to increase the diversity of the candidate centers; finally, for the clusters that fail to pair, the new split cluster is reselected by gain to pair with the original deleted cluster again to improve the pairing success rate and further reduce the objective function value. Experimental results show that compared with the I means-+ algorithm, the proposed algorithm has an average improvement in solution accuracy −k −and a more stable clustering result kunder the premise of basically equivalent operation efficiency compared with 6.47%-means and kmeans+ algorithms, the proposed algorithm has higher solution accuracy.
Enhanced Prompt Learning for Few-shot Text Classification Method
Authors: Zio Enrico , Rossi Matteo , Garcia Elena , Shuqin YE , Guangwei Zhang
Published: October 2024

An enhanced prompt learning method (EPL4FTC) for few-shot text classification task is proposed. This algorithm first converts the text classification task into the form of prompt learning based on natural language inference. Thus, the implicit data enhancement is achieved based on the prior knowledge of pre-training language models and the algorithm is optimized by two losses with different granularities. Moreover, to capture the category information of specific downstream tasks, the triple loss is used for joint optimization. The masked-language model is incorporated as a regularizer to improve the generalization ability. We evaluated our method on four Chinese and three English text classification datasets. The experimental results show that the classification accuracy of the proposed EPL4FTC is significantly better than the other compared baselines.
Optimal management of park-and-ride points and shared parking spaces in the context of autonomous driving
Authors: Yating Han , Lijun Tian , Peng Wu
Published: October 2024

This paper studies how to set the location of park-and-ride and the pricing of shared parking spaces to achieve the desired goals in the context of autonomous vehicles (AV). Assuming that a linear city does not allow single-occupancy private cars to enter the city center, travelers can only choose to park and ride or take a shared autonomous vehicle to reach the city center. Under the condition of autonomous driving, since the car can park autonomously, there are three ways to park: park the car home, park the car in a free parking space near the transfer point, and park the car in a shared parking space provided by residents near the transfer point. Starting from the two goals of profit maximization and social cost minimization, this paper explores the optimal parameter settings under the two goals respectively. The results show that the optimal park-and-ride point and the optimal shared parking space pricing under the two goals are both obtained when the supply of shared parking spaces is equal to the demand. This study can provide theoretical reference and decision-making basis for the optimal allocation of resources in the era of autonomous driving.
Post-processing algorithm of five-axis CNC machine tool under rotation angle optimization
Authors: Anjiang Cai , Chenyang Liu , Peipeng Wang
Published: October 2024

During the operation of 5-axis CNC machine tools, the angle deviation of the rotation axis greatly affects the tool path control. Therefore, a post-processing algorithm of five-axis CNC machine tool with optimized rotation angle was proposed. Firstly, the basic The structure of the machine tool was analyzed, the Hausdoff distance was used to obtain the matching error between the actual machined surface and the difference surface, and the error compensation method was used to compensate the obtained error, and the rotation axis angle of the machine tool was completed according to the processing result. Based on the optimization results, the machine tool coordinate system was established, and the machine tool post-processor was developed through the coordinate transformation results and integrated into the relevant software to realize the post-processing of the machine tool. The experimental results show that the proposed method achieves better processing results when used for post processing of machine tools.
A study on the backtesting and correlation of systemic risk in Chinese financial institutions: an empirical analysis based on MES and Delta CoVaR
Authors: Zisheng Ouyang , Xuewei Zhou
Published: October 2024

Effectively measuring the risk level of financial institutions is the key to preventing and defusing systemic risks. This paper uses the unconditional coverage test method to backtest the systemic risk of 35 listed financial institutions in China, and uses the quantile coherence method to construct a quantile frequency domain correlation network to explore the frequency domain mechanism of systemic risk correlation of financial institutions. The study found that: (1) During the financial market crisis, the Delta CoVaR commonly used systemic risk measurement indicators such as MES, CoVaR, and failed to effectively measure the systemic risk of China's financial institutions. (2) Compared with the short-term correlation layer and the medium-term correlation layer, China's financial institutions have obvious extreme risk linkage effects in the long-term correlation layer.(3) In the short-term, medium-term and long-term correlation layers, small and medium-sized financial institutions such as Ningbo Bank, Industrial Securities and Dongfang Energy all have high correlation importance. Relevant departments should pay attention to preventing the risk of "too correlated to fail" of small and medium-sized financial institutions in my country.
Improved NSGA2 algorithm for recovery of interrupted departure flights
Authors: Kejia Chen , Juntao Wu
Published: October 2024

To solve the problem of airline flight disruption caused by emergencies, the disrupted departing flights are restored, a dual-objective optimization model is constructed to minimize the total delay cost of airlines and minimize the total delay time of passengers. An adaptive non-dominated sorting genetic algorithm based on dominance intensity is designed, and three improved operations are proposed: a fast dominance sorting method, a new congestion distance and an adaptive elite retention strategy. The proposed algorithm is verified by the operation data of an airline at Fuzhou Airport. The experimental results show that compared with the traditional plan-first-serve method, the solution obtained by the proposed algorithm is greatly optimized. Compared with the constraint method, the solution time is generally lower than the constraint method and the solution result is close to the optimal result obtained by the constraint method. Compared with multi-objective optimization algorithms such as NSGA2 and MOEAD, the proposed algorithm shows better performance and can solve the problem effectively and efficiently, providing a basis for airlines to achieve optimized solutions.
GNSS/IMU integrated navigation heading angle enhancement algorithm based on dual-antenna TDCP
Authors: Jili Wang , Chen Su , Yuanyuan Wang , Yeying Dai
Published: October 2024

In the integrated navigation system of Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU), attitude estimation, especially accurate estimation of heading angle, is particularly important for real-time monitoring of vehicle driving status. However, since IMU is divergent in the altitude channel, its error will gradually accumulate if it is not accurately constrained. Therefore, the estimation accuracy of heading angle is insufficient in vehicle-mounted applications where the heading angle changes frequently. In order to solve the problem of poor navigation attitude estimation accuracy in the loose combination of traditional GNSS and IMU, this paper proposes a GNSS/IMU integrated navigation heading angle enhancement algorithm based on dual-antenna epoch carrier phase difference (TDCP). This method solves the vehicle heading angle through dual-antenna TDCP to increase the input dimension of the integrated navigation filter fusion observation value, and uses Hatch filtering and anti-error adaptive filtering to improve the observation domain pseudorange accuracy and GNSS/IMU integrated navigation positioning and attitude determination performance respectively. Experimental results show that compared with the traditional GNSS/IMU integrated navigation method, the proposed algorithm improves the positioning and speed measurement accuracy in the three-dimensional direction by and respectively 22.12%, 41.27%and the vehicle heading angle accuracy is improved 46.29%.
Signal modulation waveform recognition method based on STF-Net
Authors: Hui Ha , Xiang Gao , Xiujuan Yao , Jiangyin Fu , Wei Li , Xiaoyan Zhang
Published: October 2024

Signal modulation waveform recognition is one of the key technologies in the field of spatial spectrum cognition and an important means to realize the monitoring and control of spectrum resources of low-orbit satellites. In view of the problems of large number of parameters and high computational complexity in the current modulation waveform recognition methods based on deep learning, a lightweight signal modulation waveform recognition method based on space-time fusion network (STF-Net) is proposed. The signal is preprocessed into dual-channel data in the form of time domain and frequency domain, and the signal spatial features are extracted and feature redundancy is reduced by convolutional neural network (CNN). Then, the long short-term memory network (LSTM) is used to extract the timing information and output the recognition result. The experimental results show that when the signal-to-noise ratio is greater than 0 dB, the average recognition accuracy of the modulation waveform of the proposed method reaches 91.79%; compared with the same method, the number of parameters of the proposed method is reduced 96%and the efficiency is increased by 2.7 times.
Panoramic image quality assessment based on multi-viewport adaptive fusion
Authors: Chenxi Feng , El Gamalng Hesham , Thompson Mia , Yelong Di
Published: October 2024

Existing panoramic image quality assessment models are relatively independent when extracting local features of each viewport, resulting in high computational complexity and difficulty in characterizing the correlation between viewports using an end-to-end fusion model. To address the above problems, a quality assessment method based on feature sharing and multi-viewport adaptive fusion is proposed. Using a shared backbone network, the viewport segmentation and calculation tasks that are independent of each other in the existing method are converted to the feature domain, so that the local features of the entire image can be extracted with only one feedforward calculation. On this basis, a feature domain viewport segmentation method based on spherical uniform sampling is introduced to ensure that the pixel density of the observation space and the representation space is consistent, and semantic information is used to guide the adaptive fusion of local quality features of each viewport. The linear correlation coefficient and rank correlation coefficient on the CVIQ and OIQA datasets are both above 0.96, which is the best compared with the existing mainstream evaluation methods. Compared with the traditional evaluation method SSIM, its average linear correlation coefficient and average rank correlation coefficient on the two datasets are improved by 9.52%and respectively 8.69%; compared with the latest evaluation method MPFIQA, its average linear correlation coefficient and average rank correlation coefficient are improved by 1.71%and respectively 1.44%.
EMA fault diagnosis method based on multi-information fusion of GRU and improved attention mechanism
Authors: Chaoqin Peng , Appenzeller Joerg , Arkhipova Sampson , Juan Chen , Jiming Ma
Published: October 2024

Aiming at the problems of time series feature loss and fault information loss in traditional electromechanical servo system (EMA) fault diagnosis methods based on machine learning and deep learning, a multi-information fusion EMA fault diagnosis method based on gated recurrent unit (GRU) and improved attention mechanism is proposed. Firstly, the collected different sensor signals are divided into different channels, and the time series features of each channel signal are extracted by GRU. Then, the self-attention mechanism is introduced to further distinguish the important relationship between different time points of the signal. The multi-channel attention mechanism is further introduced to adaptively fuse the features of different channels, and finally the fault diagnosis is realized through the classifier. The experimental results based on the test bench data set show that: compared with the single sensor model, the diagnosis rate is improved 10%; compared with the model without the introduction of the attention mechanism, the diagnosis rate is improved 5.2%; compared with the classic machine learning, deep learning and the improved algorithm based on deep learning in the past two years, the diagnosis rate of the diagnosis model designed in this paper is 98.5%above, and the diagnosis effect is the best.
Resilience assessment and recovery of airport departure flights under severe weather
Authors: Wang Xinglong , Zhao Junni , Wang Jin
Published: October 2024

In order to ensure the overall performance of the airport under severe weather, scientifically evaluate the resilience of the airport's flight operations, improve flight recovery capabilities, and alleviate the impact of severe weather effectively. This article first gives the definition of airport departure flight operation. Starting from the performance of the airport departure flight operation system, it analyzes flight departure delay time, total departure delay time, departure flight normality rate and departure airport flight operation system comprehensive resilience index four indicators to evaluate the resilience changes of the system under severe weather conditions; put forward the concept of the performance recovery strategy of the airport departure flight operation system, and established the corresponding resilience model, and used the genetic algorithm to optimize the sequence of the delayed departure flights; finally, take the "721" heavy rain event in Beijing Capital International Airport in 2012 as an example to analyze the data, obtain the performance index and resilience index of the Capital Airport under the influence of heavy rain, and use the optimized departure flight schedule to compare and analyze the changes in airport departure flight operating system performance and resilience level. The results indicate that: under the influence of heavy rain, the comprehensive resilience index of the airport departure flight operation system decreased from 0.4573 to 0.0628 , and increased to 0.2223 after the rainstorm decreased; after the flight optimization and sequencing was carried out in the recovery stage of the airport departure flight operation performance, the total number of departure flights decreased. The delay time is reduced by 24.85%, the airport performance recovery speed is increased by 13.89%after optimization, and the minimum resilience index of the optimized airport departure flight operation system is increased by 13.38%, and under the same heavy rain, the system performance is given priority to restore to the initial state, indicating that the evaluation Indicators are valid.
Analysis of relaxation characteristics of multi-bolted connections under the transverse cyclic load
Authors: Li Ling , Raghunathan Anand , Taylor Sophie , Xie Miaoxia , Cheng Fuan
Published: October 2024

A modeling method under lateral cyclic loading is proposed for the complex multi-bolt connection loosening problem. Firstly, the thread stress surface is divided into n small sectors, and the self-relaxation mechanical model of multi-bolt connection is established considering the interaction between bolts. Secondly, the self-relaxation model is solved by MATLAB software and compared with Nassar model to verify the correctness of the model. Finally, considering the influence of different bolt number, hole clearance and connection spacing, the relaxation characteristics of multi-bolt connection are studied. The results show that during the loosening process, the bolt head undergoes a bending-sliding-bending cycle process, and the bolt tension shows a wave downward trend. When multiple bolts are connected, the number of loosening cycles decreases linearly with the increase of hole clearance. With the equidistant increase of bolt connection spacing, the number of bolt loosening cycles shows a decreasing trend. Compared with the number of bolts, the change of bolt hole clearance and connection spacing is more likely to affect bolt loosening.
Optimization of Multitask Scheduling for Swarm UAV System with Charging Platform
Authors: Zhijie Ke , Guoning Xu , Rong Cai , Yongxiang Li , Yanchu Yang
Published: October 2024

Swarm UAV technology have potential application in a wide range because of its ability to utilize large number, low cost and unified scheduled UAVs. Unified scheduling is tasks and resources allocation through UAV scheduling optimization, which is the key problem for swarm UAV research. Existing scheduling research mainly focuses on scheduling analysis for small-scale, short-time tasks of swarm UAVs, and rarely consider the application of charging platform. Scheduling optimization must consider the influence of charging and other factors for future UAV multi-task and long-time applications . An improved swarm UAV task scheduling method based on a unified scheduling model and improved genetic algorithms was proposed. First, the wireless charging platform resources are incorporated into the UAV working environment, and the working scenario is modeled systematically. Then, the genetic algorithm is used to optimize the task and charging platform resource assignment. Finally, the proposed method is tested and validated using simulated scenarios. Test results show that the proposed method can better adapt to changes in tasks, environment and resources, and still shows good results with relatively large swarms.
Optimizing Virtual Network Embedding for Avionics with Time-Triggered Traffic Scheduling
Authors: Feng Xiong , Qiao Li , Ji Li , Jiaying Feng
Published: October 2024

Network virtualization technology abstracts the nodes and links resources in the physical network, and enables multiple virtual networks (VNs) to share the substrate network (SN) resources through the Virtual Netw ork Embedding (VNE) method. For time-triggered Ethernet (TTE) used in avionics, a time-triggered traffic scheduling oriented virtual network embedding (TT-VNE) method is proposed. While meeting the total resource constraints of traditional VNE problem, it ensures the strict periodicity of time-triggered (TT) traffic. During the solution process, the method sorts the virtual nodes according to the TT traffic bandwidth requirements and the total bandwidth requirements of the links connected to the virtual nodes, embeds the virtual nodes using the breadth first search algorithm, and plans the virtual links in candidate shortest path aggregation. If the TT traffic in the current virtual link is not schedulable, the iterative design of local virtual node re embedding and path planning is carried out. The simulation shows that the request acceptance rate of TT-VNE is not lower than that of existing methods, and when the number of virtual network requests in the star topology exceeds 30 , its request acceptance rate is about 14.3%higher than the VNE-NTANRC-D method which only considers the network topology and resource attributes.
A graph similarity measurement algorithm combining global and local fine-grained features
Authors: Xiangjiu Che , Liang Li
Published: October 2024

Graph Similarity Computation (GSC) is not only a basic operation of graph similarity search, but also plays an important role in many application fields, including drug design, program analysis and social group identification. Since calculating the exact distance or similarity between two graphs is usually an NP-hard problem, the tradeoff between precision and speed needs to be addressed. In this paper, a pooling-based graph neural network method is proposed, which effectively integrates the coarse-grained interaction features of the two graph data and the fine -grained interaction features of the nodes between the subgraphs, and further reduce the computational cost while ensuring the accuracy. The experimental results show that the proposed method has good performance on real graph data sets. Compared with previous methods, the proposed method not only improves the accuracy but also improves the computational efficiency.
New association between Cecidochares connexa (M.) (Diptera: Tephritidae) and local parasitoids: Revisiting classical biological control
Authors: Harianto Muhammad , Triwidodo Hermanu , Agus Nurariaty , Buchori Damayanti
Published: October 2024

Lalat argentina Cecidochares connexa (L.) (Diptera: Tephritidae) diintroduksikan pada tahun 1993 di Bantimurung, Sulawesi Selatan untuk mengendalikan gulma Siam Chromolaena odorata yang merupakan gulma invasif dari Florida, Amerika Serikat. Namun, beberapa penelitian sebelumnya menunjukkan bahwa C. connexa dapat terserang oleh predator dan parasitoid lokal sehingga dapat memengaruhi efektifitas C. connexa di lapang. Penelitian ini bertujuan mempelajari asosiasi baru antara hama eksotik C. connexa dan parasitoid lokal. Penelitian dilakukan dari bulan April-Desember 2021 pada dua habitat, yaitu lahan terbuka dan perkebunan kakao, di empat desa di Kabupaten Polewali Mandar. Gulma Siam diambil secara purposive sampling, yaitu C. odorata yang berpuru. Tiga puluh C. odorata berpuru dikoleksi pada tiga lokasi berbeda di setiap habitat dan setiap desa. Puru dari setiap sampel dikelompokkan menjadi dua macam yaitu, puru yang tidak berlubang dan puru yang berlubang. Puru yang tidak berlubang dipelihara sampai serangga keluar dan spesimen serangga yang didapatkan diidentifikasi. Hasil penelitian menunjukkan bahwa rata-rata jumlah puru (baik puru sehat maupun puru  terparasit) lebih tinggi  pada lahan terbuka dibandingkan pada perkebunan kakao. Fenomena parasitasi mengindikasikan adanya interaksi dan asosiasi baru antara C. connexa dengan parasitoid lokal.  Tingkat parasitasi C. connexa di lahan terbuka (23,24%) tidak berbeda jika dibandingkan dengan parasitisasi di perkebunan kakao (28,84%). Ada empat famili parasitoid yang ditemukan yaitu Eulophidae, Braconidae, Eupelmidae, dan Ormyridae. Pada lahan terbuka ditemukan enam jenis morfo-spesies parasitoid dan pada perkebunan kakao sebanyak tiga jenis. Quadrastichus sp.1 merupakan parasitoid dominan dengan persentase parasitisasi 14% pada lahan terbuka dan 10% pada perkebunan kakao.
ภาวะผู้นำเชิงนวัตกรรมของผู้บริหารสถานศึกษาที่ส่งผลต่อความเป็นนวัตกรการศึกษาของครู สังกัดสำนักงานเขตพื้นที่การศึกษามัธยมศึกษากรุงเทพมหานคร เขต 2
Authors: ดีสวัสดิ์ เฉลิมพล , เพชรมาลัยกุล ธีระภาพ
Published: October 2024

ภูมิหลังและวัตถุประสงค์: การพัฒนาคุณภาพการศึกษามุ่งเน้นให้นำนวัตกรรมมาใช้ในการพัฒนาคุณภาพการศึกษา ครูเป็นผู้ที่สำคัญในระบบการเรียนการสอน การสร้างแนวคิดสร้างสรรค์ การเรียนรู้ การฝึกฝน และการถ่ายทอดนวัตกรรมเป็นสิ่งที่ครูยุคนี้ต้องเรียนรู้เพิ่มเติมเพื่อพัฒนาตนเองและพัฒนากระบวนการสอนของตนอยู่ตลอดเวลา หากครูมีความเป็นนวัตกรการศึกษาและมีความสามารถในการนำนวัตกรรมมาปรับใช้ในการจัดการเรียนการสอนก็จะสามารถยกระดับการศึกษาและเพิ่มศักยภาพด้านนวัตกรรมให้แก่นักเรียนได้อย่างมีประสิทธิภาพ ดังนั้น ผู้บริหารสถานศึกษาจึงต้องส่งเสริมบรรยากาศในการทำงานที่เอื้อต่อการสร้างสรรค์นวัตกรรม ในเวลาเดียวกันก็สามารถนำความคิดเชิงนวัตกรรมมาช่วยแก้ปัญหาและสร้างสรรค์นวัตกรรมต่าง ๆ ในสถานศึกษา การวิจัยครั้งนี้มีวัตถุประสงค์เพื่อ 1) เพื่อศึกษาระดับภาวะผู้นำเชิงนวัตกรรมของผู้บริหารสถานศึกษา 2) เพื่อศึกษาระดับความเป็นนวัตกรการศึกษาของครู 3) เพื่อศึกษาความสัมพันธ์ภาวะผู้นำเชิงนวัตกรรมของผู้บริหารสถานศึกษากับความเป็นนวัตกรการศึกษาของครู 4) เพื่อศึกษาภาวะผู้นำเชิงนวัตกรรมของผู้บริหารสถานศึกษาที่ส่งผลต่อความความเป็นนวัตกรการศึกษาของครู
ระเบียบวิธีการวิจัย: กลุ่มตัวอย่างคือครูในสังกัดสำนักงานเขตพื้นที่การศึกษามัธยมศึกษากรุงเทพมหานคร เขต 2 จำนวน 361 คน โดยกำหนดขนาดของกลุ่มตัวอย่างตามตารางของเครจซี่และมอร์แกน (Krejcie & Morgan, 1970) สุมตัวอย่างแบบแบ่งชั้นโดยใช้ขนาดโรงเรียนเป็นชั้น จากนั้นสุ่มอย่างง่าย เครื่องมือที่ใช้ในการวิจัยเป็นแบบสอบถามแบบมาตราส่วนประมาณค่า 5 ระดับ ซึ่งมีค่าดัชนีความสอดคล้อง IOC อยู่ระหว่าง 0.60 - 1.00 ค่าความเชื่อมั่นเท่ากับ 0.99 สถิติที่ใช้ในการวิจัย ได้แก่ ค่าเฉลี่ย ค่าส่วนเบี่ยงเบนมาตรฐาน ค่าสัมประสิทธิ์สหสัมพันธ์แบบเพียร์สันและการวิเคราะห์การถดถอยพหุคูณแบบวิธีการคัดเลือกเข้า
ผลการวิจัย: 1) ระดับภาวะผู้นำเชิงนวัตกรรมของผู้บริหารสถานศึกษาโดยรวมอยู่ในระดับมาก เมื่อพิจารณาเป็นรายด้าน พบว่าอยู่ในระดับมากทุกด้าน 2) ระดับความเป็นนวัตกรการศึกษาของครูโดยรวมอยู่ในระดับมาก เมื่อพิจารณาเป็นรายด้าน พบว่าอยู่ในระดับมากทุกด้าน 3) ภาวะผู้นำเชิงนวัตกรรมของผู้บริหารสถานศึกษามีความสัมพันธ์กับความเป็นนวัตกรการศึกษาของครูในระดับสูง อย่างมีนัยสำคัญที่ระดับสถิติที่ 0.001 โดยมีค่าสัมประสิทธิ์สหสัมพันธ์ (r) = 0.712 และ 4) ภาวะผู้นำเชิงนวัตกรรมของผู้บริหารสถานศึกษาสามารถพยากรณ์ความเป็นนวัตกรการศึกษาของครู อย่างมีนัยสำคัญทางสถิติที่ระดับ 0.001 โดยภาวะผู้นำเชิงนวัตกรรมของผู้บริหารสถานศึกษาทุกด้านร่วมกันพยากรณ์ความเป็นนวัตกรการศึกษาของครู ได้ร้อยละ 52.70
สรุปผล : ผลการวิจัยพบว่า 1) ระดับภาวะผู้นำเชิงนวัตกรรมของผู้บริหารสถานศึกษาโดยรวมอยู่ในระดับมาก    เมื่อพิจารณาเป็นรายด้าน พบว่าอยู่ในระดับมากทุกด้าน 2) ระดับความเป็นนวัตกรการศึกษาของครูโดยรวมอยู่ในระดับมาก เมื่อพิจารณาเป็นรายด้าน พบว่าอยู่ในระดับมากทุกด้าน 3) ภาวะผู้นำเชิงนวัตกรรมของผู้บริหารสถานศึกษามีความสัมพันธ์กับความเป็นนวัตกรการศึกษาของครูในระดับสูง และ 4) ภาวะผู้นำเชิงนวัตกรรมของผู้บริหารสถานศึกษาสามารถพยากรณ์ความเป็นนวัตกรการศึกษาของครู อย่างมีนัยสำคัญทางสถิติที่ระดับ 0.001 โดยภาวะผู้นำเชิงนวัตกรรมของผู้บริหารสถานศึกษาทุกด้านร่วมกันพยากรณ์ความเป็นนวัตกรการศึกษาของครู ได้ร้อยละ 52.70
Patient-Specific Orthopaedic Surgery: An Economic Analysis on 3D Printing Devices
Authors: Rezzadeh Kevin , Witek Lukasz , Hacquebord Jacques
Published: October 2024

3D printed (3DP) devices are an exciting option for clinical use by orthopaedic surgeons. This study is an economic analysis with a specific focus on personalized surgery. Specifically, we survey the existing landscape of 3DP devices in orthopaedic surgery and explore the benefits of 3DP in preoperative planning and intraoperative instrumentation. We conclude that 3DP is both a financially viable and clinically beneficial aid to the practice of orthopaedic surgeons, provided medical practices and hospitals can overcome potentially high upfront fixed costs and initial delays in clinical adoption.
Question and Answer – Integrating a robotic assistant into a high-volume orthopaedic practice.
Authors: Polikandriotis John A. , Cafferky Nathan L.
Published: October 2024

Innovative technologies are continuously being developed to improve orthopaedic surgical techniques and patient outcomes for total knee arthroplasty. One such technology that has been developed and implemented for high-volume orthopaedic practice is the use of a robotic-assistant system. The Robotic Surgical Assistant (ROSA, ZimmerBiomet, Warsaw, IN) allows for the surgeon to maintain full autonomy and control of operative decision-making while providing precise, objective, and real-time data for intraoperative adjustments, thus allowing the surgeon to both individualize and reproduce the Total Knee Replacement. This article explains a high volume Surgeon’s experience utilizing this technology, its potential benefits, and how it can be implemented for use in high-throughput private orthopaedic practice.
Conventional Vs. Robotic/Computer Assisted Total Knee Arthroplasty: A Meta-Analysis
Authors: Plaskos Christopher , Blum Christopher , Lynch Brian , Ackerman Meredith , Islam Shahidul , Lepkowsky Eric , Koenig Jan
Published: October 2024

1. INTRODUCTION

The utility of robotics in total knee arthroplasty (TKA) remains controversial. This systematic literature review with a meta-analysis was performed to determine if robotics/computer assisted (RAS/CAS) TKA improved patient reported outcome measures (PROMs) compared to conventional TKA.

2. MATERIALS AND METHODS

A systematic literature search was performed for all studies comparing outcomes between Conventional and RAS/CAS TKA. Included studies directly compared RAS/CAS TKA and conventional TKA post-operative PROMs. Outcome measures included 2011 KSS, KSS, HSS, WOMAC, and OKS. Articles that did not directly compare RAS/CAS and conventional TKA and articles with Minors criteria <12 were excluded.

3. RESULTS

Final analysis was performed on 22 studies which included 4009 TKAs with average follow-up of 52.9 months. 17 studies were evaluated for KSS scores, and moderate statistical heterogeneity was detected (τ 2 = 0.018; χ 2 = 28.29, df=16, p=0.03, I 2 =43). RAS/CAS TKA was favored over conventional TKA with a higher KSS score (SMD = 0.10; 95% CI: 0.0-0.21). 6 studies were evaluated for HSS scores and no statistical heterogeneity was detected (τ 2 = 0; χ 2 = 4.90, df=5, p=0.43, I 2 =0%), however RAS/CAS TKA was favored over conventional TKA with a higher HSS knee score (SMD = 0.31; 95% CI: 0.17-0.45).

4. CONCLUSIONS

This review shows that RAS/CAS TKA demonstrates promise in improving PROMs post-operatively compared to conventional TKA. However, it remains unclear how much these improvements translate to clinical impact. Continued analysis is warranted to further elucidate the impact of RAS/CAS on TKA.
Seroprevalence of HCV infection among Indian patients with hemodialysis
Authors:   Department of Microbiology, Meenakshi Medical College Hospital & Research Institute, Meenakshi Academy of Higher Education and Research (Deemed to be University), Kanchipuram-631552, Tamilnadu, India, Mohan Kamalraj , Jagannath Subha Vajjiravel , Sekhar Ambuja , Krishnamoorthy Akilay , Srinivasagalu Anitha , Shanmugam Sijimol , Srinivasan Senthamarai , Selvaraj Sivasankari ,   Department of Microbiology, Meenakshi Medical College Hospital & Research Institute, Meenakshi Academy of Higher Education and Research (Deemed to be University), Kanchipuram-631552, Tamilnadu, India
Published: October 2024

Hepatitis C virus (HCV) exists as the most common hepatotropic disease with a severe complication in significant patients. Maximum incidence of HCV infection occurred specifically in hemodialysis patients and they were related with increased illness and fatality rate. Therefore, it is of interest to know the seroprevalence of HCV infections in hemodialysis patients using anti-HCV and PCR diagnosis. Hence, this study was carried out at a tertiary care hospital for a period of 12 months. 92 blood samples were collected and subjected for the detection of HCV antibodies using ELISA followed by RT-PCR. Out of 92 HD patient samples, 7 were positive for Anti-HCV and 9 samples showed positive for HCV RNA. Thus, ELISA has less sensitivity with false negative rate for identification of HCV infection. Thus, hemodialysis patients should be routinely screened for HCV using RT-PCR for adequate management of the disease.
Fatores relacionados aos erros na administração de medicamentos em ambiente hospitalar: Revisão integrativa
Authors: Avila Camargo Cassiane , Aparecida Gevieschi Noetzold Eduarda , Langaro Gabriela , Priscilla Paulino dos Santos Karla , Rossarola Pozzebon Bruna , Dresch Eberhardt Thaís
Published: October 2024

Objetivo: identificar os fatores relacionados aos erros na administração de medicamentos pela equipe de enfermagem em ambiente hospitalar. Método: trata-se de uma revisão integrativa da literatura. A busca foi realizada na Biblioteca Virtual em Saúde em agosto de 2021. Foram encontradas 2581 produções e incluídas 10.  Resultados e discussão: os estudos foram realizados no Brasil com nível de evidência 4. Entre os fatores relacionados estavam: rotinas de trabalho, sobrecarga, falta de atenção, falta de conhecimento, inexperiência, interrupções, cansaço, estresse, infraestrutura, pressa, ausência ou desconhecimento de protocolos, divergências na dispensação, falta de supervisão, técnica incorreta, falta de interesse, escassez de funcionário, entre outros. Fatores individuais e estruturais influenciam diretamente nos erros de administração de medicamentos e comprometem a segurança do paciente. Conclusão: os fatores mais prevalentes nessa revisão foram a sobrecarga de trabalho, falta de atenção, interrupções, falta de conhecimento e infraestrutura.
Fundamento doctrinario y jurídico de la propiedad intelectual como derecho fundamental
Authors: Perdomo Ramirez Carlos
Published: October 2024

El presente ensayo tuvo como propósito realizar un trabajo documental en el cual se señaló la importancia de la propiedad intelectual, como fundamento doctrinario, jurídico y fundamental, producto de las clases en la especialización de Propiedad Intelectual. La propiedad intelectual se basa en reconocer que la creatividad y la innovación son valores esenciales para el desarrollo humano y social. Este reconocimiento se refleja en diversos instrumentos jurídicos internacionales y nacionales, entre ellos la Declaración Universal de Derechos Humanos y la Constitución de muchos países, que garantizan el derecho a la protección de la propiedad intelectual como un componente esencial de la dignidad humana y el progreso cultural. Por consiguiente, se asumió una investigación basada en el paradigma interpretativo, de tipo documental, que permitió la elaboración de un marco teórico conceptual, apoyado en procedimientos lógicos y mentales de toda investigación: análisis de documentos existentes, síntesis de contenidos, deducción, inducción, con el fin de darle forma al cuerpo de ideas sobre el objeto de estudio. Los hallazgos reflejaron que la propiedad intelectual protege las creaciones de la mente, como obras literarias, artísticas, invenciones, signos distintivos, entre otros, otorgando a los creadores derechos exclusivos sobre sus obras e incentivando la innovación y la creatividad. Se concluyó que la propiedad intelectual ha sido desarrollada en los últimos años con la Ley sobre el Derecho de Autor que rige en Venezuela, algunos derechos establecidos en los postulados doctrinales y la hermenéutica jurídica, todo ello dirigido a la protección de ideas producto del conocimiento del ser humano.
El papel de la educación financiera en emprendimientos de estudiantes de la Escuela Superior Politécnica Agropecuaria de Manabí, Ecuador
Authors: Sánchez Macías Silvia Patricia , Valarezo Molina María José
Published: October 2024

El éxito de los emprendimientos depende en gran medida la capacidad para gestionar adecuadamente sus recursos financieros y es necesario una correcta educación financiera. El objetivo del presente trabajo es analizar el papel de la educación financiera en los emprendimientos de estudiantes de la carrera de Administración de Empresas de la Escuela Superior Politécnica Agropecuaria de Manabí Manuel Félix López. Se desarrolló una investigación no experimental, mixta y exploratoria. Se realizó una revisión de los postulados teóricos encontrados en la literatura y se aplicó una encuesta a 75 estudiantes. Los resultados revelan insuficiencias en cuanto al manejo y comprensión de los conceptos financieros principales. A pesar de que algunos encuestados muestran comportamientos financieros positivos, como el ahorro y el conocimiento de las tasas de interés, una proporción significativa presenta deficiencias críticas en áreas fundamentales como la gestión del crédito, la planificación del ahorro y el seguimiento de la rentabilidad de sus inversiones. Al realizar un análisis de los resultados se llega a la conclusión que existe carencias en la educación financiera actual entre los estudiantes de la carrera de Administración de Empresas de la Escuela Superior Politécnica Agropecuaria de Manabí Manuel Félix López, teniendo como probables causas recursos limitados y la priorización de otras áreas de conocimientos y habilidades.