bluuebunny/crossref_metadata_2025 huggingface dataset:
Lie symmetry analysis, optimal system and exact solutions of a new (2 + 1)-dimensional KdV equation
Authors: Tiwari Ashish , Arora Rajan
Published: April 2022

This work attempts to apply the Lie symmetry approach to an updated (2+1)-dimensional KdV equation, recently updated in A.-M. Wazwaz, Nucl. Phys. B 954 (2020) 115009. The equation can be considered as one of the famous examples of the soliton equation. The infinitesimal generators for the governing equation have been found using the invariance property of Lie groups. The commutator table, adjoint table, invariant functions and one-dimensional optimal system of subalgebras are then derived using Lie point symmetries. Some group invariant solutions are derived based on various subalgebras, symmetry reductions and an optimal system. To demonstrate the physical acceptability of the results, the obtained solutions are evaluated using numerical simulation.
LBF-NoC: Learning-Based Framework to Predict Performance, Power and Area for Network-On-Chip Architectures
Authors: Kumar Anil , Talawar Basavaraj
Published: April 2022

Extensive large-scale data and applications have increasing requests for high-performance computations which is fulfilled by Chip Multiprocessors (CMP) and System-on-Chips (SoCs). Network-on-Chips (NoCs) emerged as the reliable on-chip communication framework for CMPs and SoCs. NoC architectures are evaluated based on design parameters such as latency, area, and power. Cycle-accurate simulators are used to perform the design space exploration of NoC architectures. Cycle-accurate simulators become slow for interactive usage as the NoC topology size increases. To overcome these limitations, we employ a Machine Learning (ML) approach to predict the NoC simulation results within a short span of time. LBF-NoC: Learning-based framework is proposed to predict performance, power and area for Direct and Indirect NoC architectures. This provides chip designers with an efficient way to analyze various NoC features. LBF-NoC is modeled using distinct ML regression algorithms to predict overall performance of NoCs considering different synthetic traffic patterns. The performance metrics of five different (Mesh, Torus, Cmesh, Fat-Tree and Flattened Butterfly) NoC architectures can be analyzed using the proposed LBF-NoC framework. BookSim simulator is employed to validate the results. Various architecture sizes from [Formula: see text] to [Formula: see text] are used in the experiments considering various virtual channels, traffic patterns, and injection rates. The prediction error of LBF-NoC is 6% to 8%, and the overall speedup is [Formula: see text] to [Formula: see text] with respect to BookSim simulator.
Mobile Robot Path Planning Method Based on Deep Reinforcement Learning Algorithm
Authors: Meng Haitao , Zhang Hengrui
Published: April 2022

Path planning is an important part of the research field of mobile robots, and it is the premise for mobile robots to complete complex tasks. This paper proposes a reflective reward design method based on potential energy function, and combines the ideas of multi-agent and multi-task learning to form a new training framework. The reflective reward represents the quality of the agent’s current decision relative to the past historical decision sequence, using the second-order information of the historical reward sequence. The policy or value function update of the master agent is then assisted by the reflective agent. The method proposed in this paper can easily extend the existing deep reinforcement learning algorithm based on value function and policy gradient, and then form a new learning method, so that the agent has the reflective characteristics in human learning after making full use of the reward information. It is good at distinguishing the optimal action in the corresponding state. Experiments in pathfinding scenarios verify the effectiveness of the algorithm in sparse reward scenarios. Compared with other algorithms, the deep reinforcement learning algorithm has higher exploration success rate and stability. Experiments in survival scenarios verify the improvement effect of the reward feature enhancement method based on the auxiliary task learning mechanism on the original algorithm. Simulation experiments confirm the effectiveness of the proposed algorithm for solving the path planning problem of mobile robots in dynamic environments and the superiority of deep reinforcement learning algorithms. The simulation results show that the algorithm can accurately avoid unknown obstacles and reach the target point, and the planned path is the shortest and the energy consumed by the robot is the least. This demonstrates the effectiveness of deep reinforcement learning algorithms for local path planning and real-time decision making.
Approximation of surface diffusion flow: A second-order variational Cahn–Hilliard model with degenerate mobilities
Authors: Bretin Elie , Masnou Simon , Sengers Arnaud , Terii Garry
Published: April 2022

This paper addresses the approximation of surface diffusion flow using Cahn–Hilliard-type models. We introduce and analyze a new variational phase field model that associates the classical Cahn–Hilliard energy with two degenerate mobilities and guarantees a second-order accuracy in the approximation of the sharp limit. We also introduce simple and efficient numerical schemes to approximate the solutions to three Cahn–Hilliard-type models for surface diffusion flow, including the one we propose, and we provide 2D and 3D numerical experiments that illustrate the advantages of our approach.
Weak–strong uniqueness for energy-reaction-diffusion systems
Authors: Hopf Katharina
Published: April 2022

We establish weak–strong uniqueness and stability properties of renormalized solutions to a class of energy-reaction-diffusion systems. The systems considered are motivated by thermodynamically consistent models, and their formal entropy structure allows us to use as a key tool a suitably adjusted relative entropy method. The weak–strong uniqueness principle holds for dissipative renormalized solutions, which in addition to the renormalized formulation obey suitable dissipation inequalities consistent with previous existence results. We treat general entropy-dissipating reactions without growth restrictions, and certain models with a non-integrable diffusive flux.

The results also apply to a class of (isoenergetic) reaction-cross-diffusion systems.
On linear resolvability of universal quantum dimensions
Authors: Avetisyan M. Y. , Mkrtchyan R. L.
Published: April 2022

In the study of finite (Vassiliev’s) knot invariants, Vogel introduced the so-called universal parameters, belonging to the projective plane, in particular parametrizing the simple Lie algebras by Vogel’s table. Subsequently, a number of quantities, such as some universal knot invariants and (quantum) dimensions of simple Lie algebras, have been represented in terms of these parameters, i.e., in the universal form. We prove that at the points from Vogel’s table all known universal quantum dimension formulae are linearly resolvable, i.e., yield finite answers even if these points are singular, provided one restricts them to the appropriate lines. We show that the same phenomenon takes place for another three distinguished points in Vogel’s plane — [Formula: see text], [Formula: see text] and [Formula: see text]. We also examine the same formulae on linear resolvability at the remaining 48 distinguished points in Vogel’s plane, which correspond to the so-called [Formula: see text]-objects. Among them, three points were found to be regular for all known quantum dimension formulae. Two of them happen to be sharing a remarkable similarity with the simple Lie algebras, namely, the universal formulae yield integer-valued outputs (dimensions) at the indicated points in the classical limit.
An Energy Aware Multi Slot Scheduling with Two-Layer Hexagonal Based Integrated Aggregation Approach for Underwater Wireless Sensor Networks (UWSN)
Authors: Chenthil T. R. , Jesu Jayarin P.
Published: April 2022

In the present era, Underwater Wireless Sensor Network (UWSN) is an emerging technology that involves a huge amount of sensor nodes to collect and monitor information from the underwater environment. However, the data transmission process is constrained due to the collision and energy consumption which can adversely affect the performance. Hence, there is an essential need to develop a suitable mechanism that addresses these challenges using a data aggregation-based routing mechanism in UWSN. In this paper, a Multi-Slot Scheduling with a Two-Layer Hexagonal based Integrated Aggregation model (MSS-TLHIA) is proposed that offers a prolonged lifetime with less energy consumption and collision avoidance. In this model, data aggregation is performed using the aggregator node selection process. Initially, the entire network is partitioned into several hexagonal grids using the golden ratio. This partitioning offers an improved coverage area for every node which are participating in the network. Once the network is partitioned into coverage areas called clusters, a Cluster Head (CH) is selected using the ranking-based fuzzy mechanism. Then, an aggregator node is selected in common for both the layers of the hexagonal grids. In order to prevent the energy drain of the aggregator node completely and to prolong their lifetime, the aggregator node is re-selected for every time slot. Furthermore, the occurrence of collision is avoided by the multi-slot scheduling process. Experimental results demonstrate that the performance of the proposed model is compared with other existing protocols and achieves better results in terms of network lifetime, energy consumption, collision rate, packet dropped rate, packet delivery, and data forwarding measures.
Quadratic variation and drift parameter estimation for the stochastic wave equation with space-time white noise
Authors: Assaad Obayda , Gamain Julie , Tudor Ciprian A.
Published: April 2022

We study the quadratic variations (in time and in space) of the solution to the stochastic wave equation driven by the space-time white noise. We give their limit (almost surely and in [Formula: see text]) and we prove that these variations satisfy, after a proper renormalization, a Central Limit Theorem. We apply the quadratic variation to define and analyze estimators for the drift parameter of the wave equation.
Mutually unbiased special entangled bases with Schmidt number 2d in ℂ2d+1 ⊗ ℂ4d
Authors: Yan Qianqian , Xu Dengming
Published: April 2022

In this paper, we construct mutually unbiased special entangled bases with Schmidt number [Formula: see text] in [Formula: see text]. Precisely, we first provide a necessary and sufficient condition for two special entangled bases with Schmidt number [Formula: see text] are mutually unbiased, and then use the condition to construct two mutually unbiased special entangled bases with Schmidt number 2 in [Formula: see text].
Pentavalent Cayley graphs of order twice a prime square with solvable automorphism groups
Authors: Salarian M. Reza , Abedi Fateme
Published: April 2022

A Cayley graph [Formula: see text] on a finite group [Formula: see text] is said to be normal if the right regular representation [Formula: see text] of [Formula: see text] is normal in the full automorphism group of [Formula: see text]. In this paper, we classify all connected pentavalent non-normal Cayley graphs of order [Formula: see text] with solvable automorphism groups and [Formula: see text] a prime.
A note on the dynamical system formulations in f(R) gravity
Authors: Chakraborty Saikat , Dunsby Peter K. S. , Macdevette Kelly
Published: April 2022

A number of dynamical system formulations have been proposed over the last few years to analyze cosmological solutions in f(R) gravity. The aim of this paper is to provide a brief introduction to the different approaches, presenting them in a chronological order as they appeared in the history of the relevant scientific literature. In this way, we illuminate how the shortcoming(s) of an existing formulation encouraged the development of an alternative formulation. Whenever possible, a two-dimensional phase portrait is given for a better visual representation of the dynamics of phase space. We also touch upon how cosmological perturbations can be analyzed using the phase space language.
The Rank of the 2-Class Group of Some Fields with Large Degree
Authors: Chems-Eddin Mohamed Mahmoud
Published: April 2022

Let [Formula: see text] be an integer and [Formula: see text] an odd square-free integer. We compute the rank of the 2-class group of some fields of the form [Formula: see text] when all the prime divisors of [Formula: see text] are congruent to [Formula: see text] or [Formula: see text].
Duo Property on the Monoid of Regular Elements
Authors: Hong Chan Yong , Kim Hong Kee , Kim Nam Kyun , Kwak Tai Keun , Lee Yang
Published: April 2022

We study the right duo property on regular elements, and we say that rings with this property are right DR. It is first shown that the right duo property is preserved by right quotient rings when the given rings are right DR. We prove that the polynomial ring over a ring [Formula: see text] is right DR if and only if [Formula: see text] is commutative. It is also proved that for a prime number [Formula: see text], the group ring [Formula: see text] of a finite [Formula: see text]-group [Formula: see text] over a field [Formula: see text] of characteristic [Formula: see text] is right DR if and only if it is right duo, and that there exists a group ring [Formula: see text] that is neither DR nor duo when [Formula: see text] is not a [Formula: see text]-group.
Skew Braces of Size p2 q I: Abelian Type
Authors: Acri E. , Bonatto M.
Published: April 2022

This is the first part of a series of two articles. In this paper we enumerate and classify the left braces of size [Formula: see text], where[Formula: see text] and [Formula: see text] are distinct prime numbers, by the classification of regular subgroups of the holomorph of the abelian groups of the same order. We also provide the formulas that define the constructed braces.
A Virtual Character and Nonzero Kronecker Coefficients for Self-conjugate Partitions
Authors: Li Xin
Published: April 2022

We generalize Regev's results on a virtual character of the symmetric group [Formula: see text], that is, an integer-combination of some irreducible characters. Suppose that [Formula: see text] and [Formula: see text] are integer partitions of [Formula: see text]. For the associated irreducible character [Formula: see text] of [Formula: see text], when [Formula: see text], we find another partition [Formula: see text] related to [Formula: see text] such that [Formula: see text] by the virtual character. Applying this result, we obtain a class of nonzero Kronecker coefficients by Pak et al.'s character criterion. Moreover, we discuss the effectiveness of Pak et al.'s character criterion by a concrete example.
Some Polynomial Maps with Jacobian Rank Two or Three
Authors: Yan Dan
Published: April 2022

We classify all polynomial maps of the form [Formula: see text] in the case when the Jacobian matrix of [Formula: see text] is nilpotent and the highest degree of [Formula: see text] in [Formula: see text] is no more than 1. In addition, we generalize the structure of polynomial maps [Formula: see text] to [Formula: see text].
Overcoming Divisive Strategic Environmental Assessments for Offshore Oil and Gas in Nova Scotia, Canada
Authors: Kapoor Anuja , Fraser Gail S. , Carter Angela V. , Brooks Darin
Published: April 2022

In Nova Scotia, strategic environmental assessments (SEAs) are used to scope the potential impacts of offshore oil and gas activities in the early stages of regulatory decision-making. This study examined stakeholder perceptions and involvement in SEAs for offshore oil and gas decisions on areas being opened by the provincial government for development. Stakeholder comments from 12 SEAs (2003–2019) were evaluated, and 25 interviews with strategic actors involved in the assessments were undertaken and coded. The results reveal actors in Nova Scotia are divided over the effectiveness of a sector-specific SEA: while federal–provincial governments and the regulator were satisfied with SEA function, non-governmental stakeholders questioned the credibility of the regulator as well as the intent and utility of SEAs. Policy recommendations are outlined to remedy gaps in SEA processes, notably implementing integrated management via marine spatial planning in the region.
Dynamic analysis of disturbance propagation in ecological networks with quarantine items and proportional migration
Authors: Wang Xin-Yue , Hua Jing , Li Yi-Min
Published: April 2022

In order to study the stability of the ecosystem under external attack, we regard the ecosystem as a complex network and the species disturbance after the attack as an infectious disease. We establish an ecological network disturbance propagation model based on the infectious disease model, and analyze its dynamics with the above ideas. In this paper, the species are regarded as nodes in the network, and the predator–prey relationship is regarded as the edge of the network. When the ecosystem is attacked by external forces, the disturbance can be transmitted from a species to its predator or prey through the food chain, and the disturbed species can recover themselves and then return to a stable state. At the same time, we consider adding human quarantine and protection of disturbed species. In this way, all species in the ecosystem are divided into four states: undisturbed, disturbed, quarantine and recovered. By analyzing the dynamics of disturbance propagation, the critical threshold and equilibrium point of disturbance diffusion are determined, and the local and global stability of disease-free equilibrium and endemic equilibrium are analyzed. The results show that the existence of endemic equilibrium depends on the critical threshold of disturbance propagation, which is related to the structure of food web, the propagation proportion of disturbance and the recovery proportion of species after being attacked. The larger the propagation proportion is, the weaker the resistance stability is, and the easier the disturbance propagates in the system. The higher the recovery proportion of the disturbed species, the stronger the stability of the recovery rate, and the more difficult it is for the disturbance to propagate in the system. Then we regard human protection of species as species immunity, and choose the most effective species protection measures by comparing and analyzing the threshold changes under the three protection strategies. The results show that the moderately large neighbor nodes of the disturbed species should be protected. This kind of protection measure is the most effective and it is easier to restrain the propagation of disturbance. Finally, the food webs of 85 species in a pine forest in Otago, New Zealand is selected to analyze the propagation process of disturbance by numerical simulation.
Bifurcation analysis of the cancer virotherapy system with time delay and diffusion
Authors: Liu Haicheng , Ge Bin , Liang Qiyuan , Chen Jiaqi
Published: April 2022

In this paper, we propose a cancer virotherapy model with virus lytic cycle and diffusion term. Spatiotemporal dynamic properties of the cancer virotherapy system are studied. First, by analyzing the roots distribution of the characteristic equation and transcendental equation, the conditions for the local stability of the constant equilibria of system are given. Second, we select delay as the bifurcation parameter, the existence conditions of Hopf bifurcation are given. By using the center manifold theory and normal form method of partial functional differential equation, the detailed formulae for determining the direction of Hopf bifurcation and the stability of bifurcating periodic solutions are given. Finally, some numerical simulations are given.
Dielectric Properties of Carbon-Like Nanotube Structure: Monte Carlo Study
Authors: Fadil Z. , Qajjour M. , Eraki H. , Mhirech A. , Kabouchi B. , Bahmad L. , Ousi Benomar W.
Published: April 2022

In this paper, we study the thermal and dielectric properties of a carbon-like nanotube structure. The ferrielectric mixed spin (1,3/2) Blume–Capel Ising model has been investigated, using Monte Carlo simulations under the Metropolis algorithm. We illustrate the thermal and dielectric properties of different physical parameters. Besides, we examine the effects of the exchange coupling interactions, the external longitudinal electric and the crystal fields on the transition temperature. It is found that the polarization decreases toward a transition temperature and undergoing the paraelectric phase, faster for low values of the external longitudinal electric field and the exchange coupling interactions. Besides, the total polarization versus the crystal field undergoes two principal transitions and exhibits a first-order and a second-order transitions.
Developing a Modular Tool to Simulate Regeneration Power Potential Using Orographic Wind-hovering UAVs
Authors: Gossye M. , Hwang S. , Remes B. D. W.
Published: April 2022

Applications of Unmanned Aerial Vehicles (UAVs) are often limited by flight endurance. To address the limitation of endurance, we propose a regenerative soaring method in this paper. The atmospheric energy from updrafts generated by obstacles such as hills and ships can be harvested by UAVs using a regenerative electric drivetrain. With fixed-wing aircraft, the vehicle can hover with specific wind conditions, and the battery can be recharged in the air while wind hovering. In order to research the feasibility of this regenerative soaring method, we present a model to estimate hovering locations and the amount of extractable power using the proposed method. The resulting modular regeneration simulation tool can efficiently determine the possible hovering locations and provide an estimate of the power regeneration potential for each hovering location, given the UAVs aerodynamic characteristics and wind-field conditions. Furthermore, a working regenerative drivetrain test setup was constructed and characterized that showcased promising conversion efficiencies and can be incorporated into existing UAVs easily.
Metallophthalocyanines for PDT Applications: A DFT Study
Authors: Aydogdu Seyda , Hatipoglu Arzu , Erdoğmuş Ali
Published: April 2022

Phthalocyanines (Pcs) are an important group of substances with interesting chemical and physical properties with many different application areas. The use of Pc compounds in photodynamic therapy (PDT), especially in the treatment of cancer, has gained great importance in recent years. In this study, the spectral and electronic properties of new PDT active anticancer non-peripheral tetrakis [2-mercaptoquinoline]phthalocyanine and metal complexes were examined by using Density Functional Theory (DFT). Their properties were computed in DMSO and water phases. The UV absorption spectrum study results show that the main transition responsible for the Q band of the molecular spectrum is from [Formula: see text] to [Formula: see text]. The presence of the heavier atom in the Pc cavity has the effect of enhancing the fluorescence quantum yield. In addition, photochemical singlet oxygen production of newly synthesized phthalocyanine and metal complexes was also investigated. The calculated spectral (UV, IR, NMR) and singlet oxygen yield results were compared with the experimental ones and they were in good agreement.
A Theory of Religious Affiliation
Authors: Raynold Prosper
Published: April 2022

This paper develops a theory of religious affiliation within a framework in which religious denominations are faith intermediaries and in which risk-averse laypeople consider both religious rewards and risks in making affiliation decisions. Necessary and sufficient conditions for laypeople to affiliate with denominations suggest that their perceptions of denominational trustworthiness and of the combination of risk and return implicit in the doctrinal paths denominations prescribe, are key determinants. As such, denominations use doctrinal innovation to manage their affiliations. Overall, the theory is consistent with key empirical facts and yields testable implications on both aggregate and cross-denominational variation in religious affiliation.
Fight Hardware with Hardware: Systemwide Detection and Mitigation of Side-channel Attacks Using Performance Counters
Authors: Carnà Stefano , Ferracci Serena , Quaglia Francesco , Pellegrini Alessandro
Published: April 2022

We present a kernel-level infrastructure that allows systemwide detection of malicious applications attempting to exploit cache-based side-channel attacks to break the process confinement enforced by standard operating systems. This infrastructure relies on hardware performance counters to collect information at runtime from all applications running on the machine. High-level detection metrics are derived from these measurements to maximize the likelihood of promptly detecting a malicious application. Our experimental assessment shows that we can catch a large family of side-channel attacks with a significantly reduced overhead. We also discuss countermeasures that can be enacted once a process is suspected of carrying out a side-channel attack to increase the overall tradeoff between the system’s security level and the delivered performance under non-suspected process executions.
Toward Personalized Affect-Aware Socially Assistive Robot Tutors for Long-Term Interventions with Children with Autism
Authors: Shi Zhonghao , Groechel Thomas R. , Jain Shomik , Chima Kourtney , Rudovic Ognjen (Oggi) , Matarić Maja J.
Published: April 2022

Affect-aware socially assistive robotics (SAR) has shown great potential for augmenting interventions for children with autism spectrum disorders (ASD). However, current SAR cannot yet perceive the unique and diverse set of atypical cognitive-affective behaviors from children with ASD in an automatic and personalized fashion in long-term (multi-session) real-world interactions. To bridge this gap, this work designed and validated personalized models of arousal and valence for children with ASD using a multi-session in-home dataset of SAR interventions. By training machine learning (ML) algorithms with supervised domain adaptation (s-DA), the personalized models were able to tradeoff between the limited individual data and the more abundant less personal data pooled from other study participants. We evaluated the effects of personalization on a long-term multimodal dataset consisting of four children with ASD with a total of 19 sessions, and derived inter-rater reliability (IR) scores for binary arousal (IR = 83%) and valence (IR = 81%) labels between human annotators. Our results show that personalized Gradient Boosted Decision Trees (XGBoost) models with s-DA outperformed two non-personalized individualized and generic model baselines not only on the weighted average of all sessions, but also statistically (
p
< .05) across individual sessions. This work paves the way for the development of personalized autonomous SAR systems tailored toward individuals with atypical cognitive-affective and socio-emotional needs.
Application-centric Network Management - Addressing Safety and Real-time in V2X Applications
Authors: Ernst Rolf , Stöhrmann Dominik , Bendrick Alex , Kostrzewa Adam
Published: April 2022

The current roadmaps and surveys for future wireless networking typically focus on communication and networking technologies and use representative applications to derive future network requirements. Such a benchmarking approach, however, does not cover the application integration challenge that arises from the many distributed applications sharing a network infrastructure, each with their individual topology and data structure. The paper addresses V2X networks as an important example. Crucial end-to-end application constraints including real-time and safety encourage a closer look at application interference and systematic integration. This perspective paper proposes a two-layer resource management that divides the problem into an application integration and a network management task. Valet parking with high-resolution infrastructure camera support is elaborated as a use case that overarches vehicle network and wireless network management. Experiments demonstrate the benefits of complementing the current network-centric management by an application-centric integration.
A Fall Detection Network by 2D/3D Spatio-temporal Joint Models with Tensor Compression on Edge
Authors: Li Shuwei , Man Changhai , Shen Ao , Guan Ziyi , Mao Wei , Luo Shaobo , Zhang Rumin , Yu Hao
Published: April 2022

Falling is ranked highly among the threats in elderly healthcare, which promotes the development of automatic fall detection systems with extensive concern. With the fast development of the Internet of Things (IoT) and Artificial Intelligence (AI), camera vision-based solutions have drawn much attention for single-frame prediction and video understanding on fall detection in the elderly by using Convolutional Neural Network (CNN) and 3D-CNN, respectively. However, these methods hardly supervise the intermediate features with good accurate and efficient performance on edge devices, which makes the system difficult to be applied in practice. This work introduces a fast and lightweight video fall detection network based on a spatio-temporal joint-point model to overcome these hurdles. Instead of detecting fall motion by the traditional CNNs, we propose a Long Short-Term Memory (LSTM) model based on
time-series joint-point features
extracted from a
pose extractor
. We also introduce the increasingly mature RGB-D camera and propose 3D pose estimation network to further improve the accuracy of the system. We propose to apply tensor train decomposition on the model to reduce storage and computational consumption so the deployment on edge devices can to realized. Experiments are conducted to verify the proposed framework. For fall detection task, the proposed video fall detection framework achieves a high sensitivity of 98.46% on Multiple Cameras Fall, 100% on UR Fall, and 98.01% on NTU RGB-D 120. For pose estimation task, our 2D model attains 73.3 mAP in the COCO keypoint challenge, which outperforms the OpenPose by 8%. Our 3D model attains 78.6% mAP on NTU RGB-D dataset with 3.6× faster speed than OpenPose.
Multi-Concept Representation Learning for Knowledge Graph Completion
Authors: Wang Jiapu , Wang Boyue , Gao Junbin , Hu Yongli , Yin Baocai
Published: April 2022

Knowledge Graph Completion (KGC) aims at inferring missing entities or relations by embedding them in a low-dimensional space. However, most existing KGC methods generally fail to handle the complex concepts hidden in triplets, so the learned embeddings of entities or relations may deviate from the true situation. In this article, we propose a novel
M
ulti-
c
oncept
R
epresentation
L
earning (McRL) method for the KGC task, which mainly consists of a multi-concept representation module, a deep residual attention module, and an interaction embedding module. Specifically, instead of the single-feature representation, the multi-concept representation module projects each entity or relation to multiple vectors to capture the complex conceptual information hidden in them. The deep residual attention module simultaneously explores the inter- and intra-connection between entities and relations to enhance the entity and relation embeddings corresponding to the current contextual situation. Moreover, the interaction embedding module further weakens the noise and ambiguity to obtain the optimal and robust embeddings. We conduct the link prediction experiment to evaluate the proposed method on several standard datasets, and experimental results show that the proposed method outperforms existing state-of-the-art KGC methods.
Design and Optimization of University Management Information System Based on Internet of Things and Intelligent Computing Model
Authors: Lei Qin , Li Yuxi , Yan Shi
Published: April 2022

After the preliminary design of university management information system is completed, the related systems are optimized through IOT and intelligent computing. The data of the system is monitored and trained through IOT. Through the training data, it can be seen that the maximum difference between the actual output and the predicted output data of the Internet of Things is about 4. By calculating the error percentage of the predicted and actual output values, it is found that the maximum error percentage is about 0.0632, and less than 0.1 is a normal error. The IIA model is compared with MQT model, HAMT model, and SAR model in four different function states. Through the analysis of iteration times, it is found that the best value of IIA model is 12 and the average value is 14. Compared with the other three models, the convergence degree is higher, the calculation time is shorter, and the calculation results are more accurate. In order to explore whether the IIA optimization model proposed in this paper can run normally, the performance of the optimization system is tested by dividing the number of users into three groups: 100, 400, and 1000, and testing and analyzing the average response time, peak traffic response time, and test conclusion. According to the data, the average response time of IIA optimization model system is 2.031, 3.211, and 4.421. The average response time of related systems in traditional universities is 3.075, 4.563, and 5.097, respectively, and the test conclusions are all passed. According to the data analysis, it is known that the average response time of IIA optimization system is greatly reduced compared with the traditional system, which shows that this model has a good optimization effect for management information system. Finally, the success rate of IIA optimization model and traditional system is analyzed, which shows that performance is greatly improved after IIA optimization model is optimized. At present, the management information system is of great help to the entry of college students’ information. The management information system is used to enter, classify, and manage student information. The design and optimization of the system through IOT technology and intelligent computing-related models are helpful in improving the average response time of the system and improving the data processing capability of the system, which is convenient for register student information.
Dynamic System Analysis of Investment in Both Production and R&D with Time Delay
Authors: Gao Debao
Published: April 2022

This paper, according to the process of capital return, establishes a differential dynamics model of investment with two time delays. When both time delays are zero, it is proved that the model is positively invariant, uniformly bounded, and globally asymptotically stable by using the comparison principle and Bendixson–Dulac theorem. When at least one time delay is not zero, according to Hopf bifurcation theorem, the conditions of local asymptotic stability and existence of periodic solution of investment model are obtained. By using the normal form theory and the center manifold theory, the discriminant formula of periodic solution property of investment model is given. Under the condition of controlled time delay, the model is numerically simulated to verify the correctness of relevant analytical conclusions. Therefore, the investment model describes the dynamic process and development trend of project investment quite closely.
Hypertuned Deep Convolutional Neural Network for Sign Language Recognition
Authors: Mannan Abdul , Abbasi Ahmed , Javed Abdul Rehman , Ahsan Anam , Gadekallu Thippa Reddy , Xin Qin
Published: April 2022

Sign language plays a pivotal role in the lives of impaired people having speaking and hearing disabilities. They can convey messages using hand gesture movements. American Sign Language (ASL) recognition is challenging due to the increasing intra-class similarity and high complexity. This paper used a deep convolutional neural network for ASL alphabet recognition to overcome ASL recognition challenges. This paper presents an ASL recognition approach using a deep convolutional neural network. The performance of the DeepCNN model improves with the amount of given data; for this purpose, we applied the data augmentation technique to expand the size of training data from existing data artificially. According to the experiments, the proposed DeepCNN model provides consistent results for the ASL dataset. Experiments prove that the DeepCNN gives a better accuracy gain of 19.84%, 8.37%, 16.31%, 17.17%, 5.86%, and 3.26% as compared to various state-of-the-art approaches.
Pattern Changes in the Heart Rate Variability of Patients Undergoing Coronary Artery Bypass Grafting Surgery
Authors: Van Thanh Ngo , Hien Nguyen Sinh , Son Pham Nguyen , Son Pham Truong
Published: April 2022

Introduction. Coronary artery bypass grafting (CABG) with extracorporeal circulation is a key therapy for coronary artery disease (CAD). However, cardiovascular events and cardiac arrhythmias may still occur in these patients following surgery. Many studies have demonstrated a correlation between cardiac arrhythmias and heart rate variability (HRV). This study aimed to establish the temporal change pattern of HRV observed following CABG. Methods. A prospective method was used to study 119 consecutive patients with stable CAD who were assessed using 24-hour Holter recordings 2 days before CABG and 1 week, 3 months, and 6 months after the surgery at Hanoi Heart Hospital from June 2016 to August 2018. Main results: All the time-domain and frequency-domain parameters of HRV decreased precipitately after CABG and were mostly recovered 3 months postoperatively. The percentage of decreased HRV before surgery was 28.6% and 51.8% after 7 days, 19.6% after 3 months, and 12.7% after 6 months. ASDNN and SDNN before and after surgery had the highest rates of change. Conclusion. The early decrease in HRV observed 7 days after CABG may be related to the acute effects of the surgery. The recovery of HRV at 3 months after surgery, regardless of the preoperative state of the patients, implies that the autonomic nervous system (ANS) disorder may be improved at this time. At 6 months after surgery, the autonomic nervous injury was recovered in combination with improvement of reperfusion, resulting in improvement in almost all HRV indices compared with those indices preoperatively.
Association of Polymorphisms in miR146a, an Inflammation-Associated MicroRNA, with the Risk of Idiopathic Recurrent Spontaneous Miscarriage: A Case-Control Study
Authors: Salimi Saeedeh , Sargazi Saman , Mollashahi Behrouz , Heidari Nia Milad , Mirinejad Shekoufeh , Majidpour Mahdi , Ghasemi Marzieh , Sargazi Sara
Published: April 2022

It has been established that microRNAs (miRNAs) are involved in the regulation of immune responses and serve as biomarkers of inflammatory diseases as well as recurrent spontaneous miscarriage (RSM). Herein, we aimed to study the relationship between three functional miR146a gene polymorphisms with idiopathic RSM (IRSM) susceptibility. We recruited 161 patients with IRSM and 177 healthy women with at least one live birth and without a history of abortion. Genotyping was performed using RFLP-PCR and ARMS-PCR methods. We found that the rs6864584 T/C decreased the risk of IRSM under dominant TT+TC vs. CC (

OR
=
0.029

) and allelic C vs. T (

OR
=
0.028

) contrast models. Regarding rs2961920 A/C and rs57095329 A/G polymorphisms, the enhanced risk of IRSM was observed under different genetic contrasted models, including the codominant CC vs. AA (

OR
=
2.81

for rs2961920) and codominant GG vs. AA (

OR
=
2.36

for rs57095329). After applying a Bonferroni correction, haplotype analysis revealed a 51% decreased risk of IRSM regarding the ACA genotype combination. This is the first study reporting that miR146a rs57095329 A/G, rs2961920A/C, and rs6864584 T/C polymorphisms are associated with the risk of IRSM in a southern Iranian population. Performing replicated case-control studies on other ethnicities is warranted to outline the precise effects of the studied variants on the risk of gestational trophoblastic disorders.
Analysis of the Role and Mechanism of ZEB1 in Regulating Cervical Carcinoma Progression via Modulating PD-1/PD-L1 Checkpoint
Authors: Lin Yuhong , Wang Xiaoxian
Published: April 2022

Background. Cervical carcinoma (CC) is a common and highly malignant tumor in women. The involvement of zinc finger E-box binding homeobox 1 (ZEB1) in many kinds of tumors has been well-documented; however, its role and mechanism in CC remain to be clarified. Objective. This study investigated the mechanism of ZEB1 in modulating the growth and metastasis of CC cells. Methods. The expression of ZEB1 in CC tissues and adjacent normal counterparts was determined by reverse transcription-polymerase chain reaction (RT-PCR). The correlation between ZEB1 and patient clinicopathological indexes was analyzed. In vitro, gain and loss functions of ZEB1 were performed in C-33A and HeLa cell lines. The proliferation, migration, and invasion of CC cells were detected by Cell Counting Kit-8 (CCK-8) assay and transwell assay, respectively. The expression levels of apoptosis-related proteins such as BCL2-associated X (Bax), B-cell lymphoma-2 (Bcl2), and Caspase-3, as well as epithelial-mesenchymal transition (EMT)-associated proteins including E-cadherin, Vimentin, and Snail, were measured by Western blotting. In addition, the targeting relationship between ZEB1 and programmed death 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) was predicted by bioinformatics and further verified by dual-luciferase reporter assay. Results. ZEB1 was significantly up-regulated in CC tissues compared with normal counterparts. ZEB1 overexpression promoted the migration, proliferation, and invasion of CC cells and inhibited apoptosis, while knocking down ZEB1 contributed to the opposite effects. In addition, experiments on related mechanisms confirmed that ZEB1 targeted the 3’EUTR terminal of PD-1/PD-L1 and negatively regulated its expression. And an interaction between ZEB1 and PD-1/PD-L1 was identified. Conclusion. ZEB1 can promote the proliferation and metastasis of CC cells via modulating the PD-1/PD-L1 checkpoint.
Optimizing the Training Mode of Swimmers Based on Deep Learning and Data Mining Technology
Authors: Liu Shuai , Sun Shuze
Published: May 2022

Data mining is the most popular research direction of information science nowadays. It revolves around all the data and continuously excavates some of the potentially valuable data information. This information has great application value. This is a dynamic and interactive process. Deep learning is an important branch of machine learning, and the introduction of deep learning theory has promoted the development of artificial intelligence. Swimming is a rather special sport. From the perspective of special characteristics, the body is in an unstable state during swimming, there is no fixed fulcrum, and it is necessary to fight against water resistance in the water. In the case of maintaining the best streamlined and reducing resistance as much as possible, through the power forward movement method brought by rowing and kicking, the training of swimming-specific strength is also unique to the project. In this study, the application of deep learning and data mining technology to the optimization of swimming athletes’ training mode is proposed, and algorithms such as neural network and support vector machine are expounded. This paper adopts two different training methods. That is, the experimental group adopts functional training. The control group used traditional training to compare the special performance of the two groups of athletes before and after physical training. In this way, we can understand the changes of traditional training and functional training on the performance of swimmers, compare the methods of the two groups of athletes and provide swimmers with more scientific and effective training methods. The experimental results of this paper show that through the data mining technology experiment, the main item test scores of the two groups of subjects before and after the experiment were

p
=
0.004

, which was less than 0.01, showing a very significant difference. This shows that the use of deep learning and data mining technology has a very significant effect on swimming athletes in training to improve performance.
Risk Factors for TERT Promoter Mutations with Papillary Thyroid Carcinoma Patients: A Meta-Analysis and Systematic Review
Authors: Mao Jingxin , Huang Xingliang , Okla Mohammad K. , Abdel-Maksoud Mostafa A. , Mubarak Ayman , Hameed Zahid , Noreen Razia , Chaudhary Aqsa , Ghazanfar Shakira , Liao Yixuan , Hameed Yasir , Li Chen
Published: April 2022

Whether TERT promoter mutation is related to more aggressive clinicopathologic features and worse outcomes in papillary thyroid carcinoma patients (PTCs) is still variable and controversial. Our intention was to investigate the risk or prognostic factors that may additionally predict the TERT promoter mutation doable of these lesions and new prevention techniques in PTCs. A total of 2,539 PTC patients with 11.50% TERT mutation have been analyzed using Revman 5.3 software in this study. The PubMed and Embase databases were systematically searched for works published until November 9, 2021. The following variables had been associated with an extended chance of TERT promoter mutation in PTC patients:

age
<
45

years (

MD
=
10.93

,

95
%
CI
=
7.25
–
14.61

);

gender
=
male

(

pooled
 
OR
=
1.63

,

95
%
CI
=
1.17
–
2.28

);

tumor
 
size
>
1
 
cm

(

MD
=
0.56

,

95
%
CI
=
0.34
–
0.77

); lymph node metastasis (

pooled
 
OR
=
1.29

,

95
%
CI
=
0.93
–
1.79

); vascular invasion (

pooled
 
OR
=
1.78

,

95
%
CI
=
0.83
–
3.84

); extrathyroidal extension (

pooled
 
OR
=
2.00

,

95
%
CI
=
1.32
–
3.02

); distant metastasis (

pooled
 
OR
=
1.46

,

95
%
CI
=
1.04
–
2.04

); advanced TNM stage (

pooled
 
OR
=
3.19

,

95
%
CI
=
2.28
–
4.45

). In addition, multifocality (

pooled
 
OR
=
0.67

,

95
%
CI
=
0.14
–
3.24

) had no affiliation with TERT promoter mutation in PTC patients. Our finding showed that

age
<
45

years, male,

tumor
 
size
>
1
 
cm

, lymph node metastasis, vascular invasion, and superior/advanced TNM stage were dangerous elements for TERT promoter mutation of worse effect in PTCs while that multifocality was once negatively correlated. TERT promoter mutation is drastically associated with recurrence and PTC-related mortality.
Numerical Analysis of Two Kinds of Nonlinear Differential Equations Based on Computer Energy Simulation
Authors: Li Feng
Published: April 2022

In order to solve some shortcomings, such as the traditional integer order calculus theoretical model is in good agreement with the numerical experimental results, the fractional order calculus model in many fields such as modern engineering calculation is proposed, which has been paid more attention and applied than the integer order calculus model. In such problems, nonlinear fractional differential equations sometimes bring us many unexpected surprises, so as to get unexpected conclusions about the description of the problem. The experiment shows that when the time

t
=
0.5

, the error between them is 0.0305, and the error is slightly larger. In this case, we can reduce the overall error by adding a new term of the decomposition sequence, and the approximate analytical solution can be closer to the exact solution, which verifies the effectiveness of the experiment.