bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Impacts of Cyber Security on Social Life of Various Social Media Networks on Community Users. A Case Study of Lagos Mainland L.G.A of Lagos State
Authors: Diallo Oumar Bella , Xie Paul 
Published: July 2022

AbstractThis study investigates the impacts of cyber Security on social life on community users Lagos L.G.A. The survey research was designed and adopted to describe this study. The sample for this research consists of one hundred and twenty undergraduate students randomly selected from respondents in their different homes using a simple technique. A structured questionnaire titled was developed and validated. It has a reliability coefficient of 0.72 using the test and re-test method. Descriptive statistics of frequency count and simple percentages were used to analysis the research question. This study is based on finding solution, it was affirmed that result from the demographic variable of respondents by age, sex, most visited social networks, duration of visitation, hours spent on social networks daily and people with the gadget that can access the internet. Results show that communities aware of the effects of cyber security on social media platforms are people between the ages of 21–25 years, male and female community dwellers have access to social network platforms, WhatsApp represents the most visited social platforms by people. The research questions show that social media platforms significantly influence the social life of community users in Lagos mainland L.G.A.
Analyzing the Structural Complexity of Software Systems Using Complex Network Theory
Authors: Du Juan 
Published: July 2022

AbstractSoftware systems have nearly been used in all walks of life, playing an increasingly important role. Thus, how to understand and measure complex software systems has become an ever-important step to ensure a high-quality software system. The traditional analysis of software system structure focuses on a single module. However, the traditional software structural metrics mainly focus on analyzing the local structure of software systems and fail to characterize the properties of software as a whole. Complex network theory provides us with a new way to understand the internal structure of software systems, and many researchers have introduced the theory of complex networks into the examination of software systems by building software networks from the source code of software systems. In this paper, we combine software structure analysis and complex network theory together and propose a SCANT (Software Complexity Analysis using complex Network Theory) approach to probe the internal complexity of software systems.
Cluster-Based Three-Dimensional Particle Tracking Velocimetry Algorithm: Test Procedures, Heuristics and Applications
Authors: Ma Qimin , Lin Yuanwei , Zhang Yang 
Published: July 2022

AbstractParticle tracking velocimetry (PTV) algorithm based on the concept of particle cluster is investigated and improved. Firstly, an artificial test flow is constructed, and a dimensionless parameter CPTV is introduced to characterize the difficulty for the PTV reconstruction. Secondly, the heuristics that particle-cluster based algorithms must follow are summarized, and a three-dimensional cluster-based PTV incorporating the Delaunay Tessellation is proposed and tested by using the artificial flow. The criteria property of CPTV is then analysed and verified. Combining the proposed algorithm with a three-dimensional particle detection system, two particle flows are successfully reconstructed, therefore verifying the practicality of the algorithm.
Vehicle Collision Prediction Model on the Internet of Vehicles
Authors: Qian Shenghua 
Published: July 2022

AbstractAn active collision prediction model on the Internet of Vehicles is proposed. Through big data calculation on the cloud computing platform, the model predicts whether the vehicles may collide and the time of the collision, so the server actively sends warning signals to the vehicles that may collide. Firstly, the vehicle collision prediction model preprocesses the data set, and then constructs a new feature set through feature engineering. For the imbalance of the data set, which affects predictive results, SMOTE algorithm is proposed to generate new samples. Then, the LightGBM algorithm optimized by Bayesian parameters is used to predict the vehicle collision state. Finally, for the problem of low accuracy in predicting the collision time, the time prediction is transformed into a classification problem, and the Bayesian optimization K-means algorithm is used to predict the vehicle collision time. The experimental results prove that the vehicle collision prediction model proposed in this paper has better results.
Emotional Analysis and Application of Business Space Based on Digital Design
Authors: Wang Yaying , Dou Jiahui 
Published: July 2022

AbstractAs social science and technology progressing, people pay more attention to themselves. Jewelry, whether as a daily design or exquisite art, deeply carries individual feeling. Commercial space design, as an important embodiment of tolerance and foil, could show its value and meet people's emotional needs. Based on jewelry store design, this paper studies the emotional design contained in digital commercial space to enrich the emotional experience in space design. Through the construction and design of jewelry store space, it can better convey the value and emotion of goods, and apply emotional elements to the layout, color and form of digital commercial space, so as to build a digital commercial space full of emotion and design [1].
Study on the Analysis Method of Ship Surf-Riding/Broaching Based on Maneuvering Equations
Authors: Zhang Baoji , Fu Lupeng 
Published: July 2022

AbstractIn order to understand the mechanism of the surf-riding/broaching profoundly, the four- degree- of-freedom(4DOF) maneuvering equation (surge, sway, yaw and roll) is simplified to a one- degree-of-freedom (1DOF) equation, and the fourth-order Runge-Kutta method is used to integrate a 1DOF surge equation in the time domain to analyze the two motion states of the ship during the surging and surf-riding. The critical Froude number is calculated using the Melnikov method. Taking a fishing boat as an example, the ship’s surf-riding/broaching phenomenon is simulated under the condition of wavelength-to-ship-length ratio and wave steepness, 1 and 1/10 respectively, providing technical support for the formulation of the second generation intact stability criteria.
Research on Virtual and Real Fusion Maintainability Test Scene Construction Technology
Authors: Zhang Yi , Ge Zhexue , Li Qiang 
Published: July 2022

AbstractThe construction of maintenance test scenes is the premise of accurate assessment of equipment maintenance. In order to reduce the cost and simulate the actual maintenance scene of the product with high fidelity, the construction method of virtual and real fusion maintainability test scene based on partial physical devices is studied in depth. The position and posture of the physical equipment are recognized by binocular vision, and the virtual environment is registered around the physical equipment. Firstly, the ORB (Oriented FAST and Rotated BRIEF) feature extraction of the physical product is carried out and compared, the ICP (iterative closest point) method is then used to perform the matching of physical product features and digital prototype features. Secondly, the virtual maintenance environment is register accurately. Thirdly, the experimental evaluation method of qualitative and quantitative indexes of virtual and real fusion maintainability is formulated. Finally, a case study of a virtual and real fusion maintainability test is carried out with an engine as an example, which verifies the effectiveness and feasibility of the maintenance evaluation based on the virtual and real fusion test scene.
Cascaded GLRT Radar/Infrared Lidar Information Fusion Algorithm for Weak Target Detection
Authors: Wu Peixuan , Du Xiaoyong , Hu Weidong 
Published: July 2022

AbstractTo deal with the problem of weak target detection, a cascaded generalized likelihood ratio test (GLRT) radar/infrared lidar heterogeneous information fusion algorithm is proposed in this paper. The algorithm makes full use of the target characteristics in microwave/infrared spectrum and the scanning efficiency of different sensors. According to the correlation of target position in the multi-sensor view field, the GLRT statistic derived from the radar measurements is compared with a lower threshold so as to generate initial candidate targets with high detection probability. Subsequently, the lidar is guided to scan the candidate regions and the final decision is made by GLRT detector to discriminate the false alarm. To get the best detection performance, the optimal detection parameters are obtained by nonlinear optimization for the cascaded GLRT Radar/Infrared lidar heterogeneous information fusion detection algorithm. Simulation results show that the cascaded GLRT heterogeneous information fusion detector comprehensively utilizes the advantages of radar and infrared lidar sensors in detection efficiency and performance, which effectively improves the detection distance upon radar weak targets within the allowable time.
CTran_DA: Combine CNN with Transformer to Detect Anomalies in Transmission Equipment Images
Authors: Zhou Honghui , Qin Ruyi , Wu Jian , Qian Ying , Ju Xiaoming 
Published: July 2022

AbstractWith the development of the State Grid, the power lines, equipment and transmission scale are expanding. In order to ensure the stability and safety of electricity, it is necessary to patrol and inspect the power towers and other equipment. With the help of deep learning, neural networks can be used to learn the features in patrol image. In this paper, feature learning model named CNN Transformer Detect Anomalies (CTran_DA) is proposed to detect anomalies in patrol images. CTran_DA uses CNN to learn local features in the image, and uses Transformer to learn global features. This paper innovatively combines the advantages of CNN and Transformer to learn the local details as well as the global feature associations in images. By comparing experiments on out self-constructed dataset, the model outperforms state-of-the-art baselines. Moreover, the Floating Point Operations (FLOPs) and parameters of the model in this paper are smaller than other algorithms. In general, CTran_DA is an efficient and lightweight model to detect anomalies in images.
Implementation and Application of Embedded Real-Time Database for New Power Intelligent Terminal
Authors: Shi Yingjie , Wang Xiang , Wang Wei , Zhang Huayun , Jiang Shusong 
Published: July 2022

AbstractAn implementation method of embedded real-time database is proposed. The lightweight high matching of power model is realized through tree structure. The resource consumption of real-time database in embedded device environment is reduced by means of separated storage and non independent process deployment. The efficient access of measuring point data is realized through internal mapping rules and improved breadth first search algorithm. Experiments show that the embedded real-time database realized by this method has good performance and low energy consumption, and is suitable for intelligent terminal equipment in new power system.
Analysis of Purchasing Power Data of Department Store Members and Design of Effective Management Model
Authors: Li Bo , Jiang Henry L. , Yan Hanhuan , Qi Yishan , Gan Zhiwang 
Published: July 2022

AbstractThis paper focuses on the consumption situation and discounting strategies of members in large department stores. On this basis, reasonable strategies and suggestions for discounting activities in department stores are proposed. It needs to determine the consumption habits of members, customer value, life cycle, discount effect and other information. The mathematical model was established to calculate the activation rate of non-active members in the life cycle of members, that is, the possibility of transforming from inactive members to active members. Based on the actual sales data, the relationship model between the activation rate and shopping mall promotion was determined. Generally speaking, the higher the commodity price is, the higher the profit will be. IA regression model of activation rate and promotion activities is developed. The appraisal index of market promotion activities is established in terms of both discounts and integral. Lasso regression is used for variable screening, and the correlation between activation rate and the above indicators is studied.
Analysis of Subway Braking Performance Based on Fuzzy Comprehensive Evaluation Method
Authors: Peng Hua , He Yixin 
Published: July 2022

AbstractIn the process of subway operation, the braking system is a complex system, and its state detection is for high data accuracy and state positioning accuracy According to the structure of the braking system and the principle of the braking method, the basic braking performance parameters of the system are analyzed, combined with the abnormal state of the subway brake cylinder pressure data, the braking process is divided into two stages: brake cylinder pressure establishment and peak stability. And define the six characteristic parameters of 90% brake cylinder pressure establishment time, special slope period time, stable pressure value, stable pressure standard deviation, maximum value and minimum value. Aiming at the braking process performance, a data mining theory is proposed, and software based on the fuzzy comprehensive evaluation method is written to analyze the deterioration of the braking performance of subway vehicles. The actual on-board data is used as an example to verify the reliability of the theory.
Some Problems of Complex Signal Representation
Authors: Xu JingBo 
Published: July 2022

AbstractThe time domain signal is based on the decomposition of the unit step signal, the complex signal is represented by the Heaviside Function, and the problem of the definition of the original jump time in the new function is proposed, based on the analysis and comparison of simple signal and complex signal in time domain and frequency domain, the problems needing attention in using $$\varepsilon (t)$$

ε
(
t
)

to express signal are put forward. It is concluded that no definition or special definition of the “0” moment in the original unit step signal does not affect the composition of the composite function.
Design and Finite Element Analysis of Magnetorheological Damper
Authors: Song Yunyun , Yang Xiaolong 
Published: July 2022

AbstractIn order to solve the problem that the output damping force of magnetorheological Damper is not large enough and the adjustable range is small, a bypass magnetorheological Damper is designed in this paper. The Valve is connected in a hydraulic cylinder with pipes to form a controllable magnetorheological Damper device. Two structures are designed by adding non-magnetic materials to the structure so that the magnetic field lines pass vertically through the damping gap as much as possible. One is to use two coils and add a non-magnetic material above the coil, and the other is to use only one coil and add a non-magnetic material above the coil. The finite element method is used to simulate and analyze the parameters of two structures which affect the damping performance, and the results are discussed. The results show that more magnetic force lines can pass through the damping channel vertically by adding non-magnetic material to the structure, which can increase the damping force and adjustable coefficient.
Optimizing Performance of Image Processing Algorithms on GPUs
Authors: Zhou Honghui , Qin Ruyi , Liu Zihan , Qian Ying , Ju Xiaoming 
Published: July 2022

AbstractThe application of machine learning algorithms in the field of power grid improves the service level of power enterprises and promotes the development of power grid. NVIDIA Volta and Turing GPUs powered by Tensor Cores can accelerate training and learning performance for these algorithms. With Tensor Cores enabled, FP32 and FP16 mixed precision matrix multiplication dramatically accelerates the throughput and reduces AI training times. In order to explore the cause of this phenomenon, we choose a convolutional neural network (CNN), which is widely used in computer vision, as an example and show the performance characteristics with tensor core on general matrix multiplications and convolution calculations as benchmark. Building a CNN based on cuDNN and TensorFlow, we analyze the performance of CNN from various aspects and optimize performance of it by changing the shape of convolution kernel and using texture memory, etc. The experimental results prove the effectiveness of our methods.
Point Cloud Registration of Road Scene Based on SAC-IA and ICP Methods
Authors: Liu Yan , Su Hu , Lei Yu , Zou Fan 
Published: July 2022

AbstractRegistration of point cloud data obtained by vehicle-mounted LiDAR is necessary process to establish high-precision road scene 3D model automatically. This paper presents a set of multi-line LiDAR point cloud registration method in road scenarios. Firstly, the obtained original point cloud data are pre-processed according to the characteristics of multi-line LiDAR point cloud. Then an initial registration algorithm (SAC-IA) with sampling consistency based on fast point feature histogram (FPFH) is used to achieve the coarse registration for two frame point clouds. Lastly, ICP algorithm optimized by KD-tree is used for precise registration and global road point cloud model can be obtained by iterative registration. In order to verify the method, actual road point cloud data are collected. The experimental results show that the method is feasible and its registration accuracy can meet the requirements of road model.
Altered m6A RNA methylation contributes to hippocampal memory deficits in Huntington’s disease mice
Authors: Pupak Anika , Singh Ankita , Sancho-Balsells Anna , Alcalá-Vida Rafael , Espina Marc , Giralt Albert , Martí Eulàlia , Ørom Ulf Andersson Vang , Ginés Silvia , Brito Verónica 
Published: July 2022

AbstractN6-methyladenosine (m6A) regulates many aspects of RNA metabolism and is involved in learning and memory processes. Yet, the impact of a dysregulation of post-transcriptional m6A editing on synaptic impairments in neurodegenerative disorders remains unknown. Here we investigated the m6A methylation pattern in the hippocampus of Huntington’s disease (HD) mice and the potential role of the m6A RNA modification in HD cognitive symptomatology. m6A modifications were evaluated in HD mice subjected to a hippocampal cognitive training task through m6A immunoprecipitation sequencing (MeRIP-seq) and the relative levels of m6A-modifying proteins (FTO and METTL14) by subcellular fractionation and Western blot analysis. Stereotaxic CA1 hippocampal delivery of AAV-shFTO was performed to investigate the effect of RNA m6A dysregulation in HD memory deficits. Our results reveal a m6A hypermethylation in relevant HD and synaptic related genes in the hippocampal transcriptome of Hdh+/Q111 mice. Conversely, m6A is aberrantly regulated in an experience-dependent manner in the HD hippocampus leading to demethylation of important components of synapse organization. Notably, the levels of RNA demethylase (FTO) and methyltransferase (METTL14) were modulated after training in the hippocampus of WT mice but not in Hdh+/Q111 mice. Finally, inhibition of FTO expression in the hippocampal CA1 region restored memory disturbances in symptomatic Hdh+/Q111 mice. Altogether, our results suggest that a differential RNA methylation landscape contributes to HD cognitive symptoms and uncover a role of m6A as a novel hallmark of HD.
Nonresonant bilinear forms for partially dissipative hyperbolic systems violating the Shizuta–Kawashima condition
Authors: Bianchini Roberta , Natalini Roberto 
Published: July 2022

AbstractIn the context of hyperbolic systems of balance laws, the Shizuta–Kawashima coupling condition guarantees that all the variables of the system are dissipative even though the system is not totally dissipative. Hence it plays a crucial role in terms of sufficient conditions for the global in time existence of classical solutions. However, it is easy to find physically based models that do not satisfy this condition, especially in several space dimensions. In this paper, we consider two simple examples of partially dissipative hyperbolic systems violating the Shizuta–Kawashima condition (SK) in 3D, such that some eigendirections do not exhibit dissipation at all. We prove that if the source term is nonresonant (in a suitable sense) in the direction where dissipation does not play any role, then the formation of singularities is prevented, despite the lack of dissipation, and the smooth solutions exist globally in time. The main idea of the proof is to couple Green function estimates for weakly dissipative hyperbolic systems with the space–time resonance analysis for dispersive equations introduced by Germain, Masmoudi and Shatah. More precisely, the partially dissipative hyperbolic systems violating (SK) are endowed, in the nondissipative directions, with a special structure of the nonlinearity, the so-called nonresonant bilinear form for the wave equation (see Pusateri and Shatah, CPAM 2013).
Cardiac Arrest Center – Zertifizierung stärkt Patientenzufluss durch den Rettungsdienst
Authors: Rott Nadine , Wingen Sabine , Müller Dirk , Böttiger Bernd W. 
Published: July 2022

Zusammenfassung
Hintergrund
Seit 2015 empfehlen die internationalen Reanimationsleitlinien die Implementierung von spezialisierten Krankenhäusern (sog. Cardiac Arrest Center, CAC) für die Versorgung von Patienten mit außerklinischem Herz-Kreislauf-Stillstand.

Ziel
Ziel der Studie war es, den potenziellen Einfluss der Zertifizierung von Kliniken als CAC auf das Einweisungsverhalten von Notärzten und Rettungsfachpersonal bei prähospital reanimierten Patienten zu untersuchen.

Methoden
Eine webbasierte anonyme Befragung mit 20 Items wurde vom 15.05. bis zum 15.06.2018 in Deutschland durchgeführt. Zielgruppen waren im Rettungsdienst tätige Notärzte sowie Rettungsfachpersonal.

Wesentliche Ergebnisse
Von 437 Teilnehmern wurden die Ergebnisse von 378 Befragten (n = 292 Notärzte, n = 86 Rettungsfachpersonen) in die statistische Analyse eingeschlossen. 75,1 % (n = 284) gaben an, dass die CAC-Zertifizierung von Krankenhäusern bei ihrem künftigen Einweisungsverhalten von Patienten mit präklinischem Herz-Kreislauf-Stillstand eine Rolle spielen würde. 78,3 % (n = 296) erwarteten, dass die CAC-Zertifizierung zu einer Verbesserung der Patientenversorgung führen wird. 78,8 % (n = 298) befürworteten die Einführung der CAC-Zertifizierung. Die Befragten würden eine zusätzliche Transportzeit von 16,3 min (95 %-KI: 15,2–17,3) akzeptierten, um ein CAC zu erreichen.

Fazit
Die Zertifizierung von Kliniken als CAC hat das Potenzial, die Entscheidung des Rettungsdienstpersonals bei der Zuweisung von Patienten mit einem präklinischen Herz-Kreislauf-Stillstand zu beeinflussen. Aufgrund der limitierten zusätzlich akzeptablen Transportzeit zur Erreichung eines CAC bedarf es eines bundesweiten, engen Netzes zertifizierter Kliniken.