bluuebunny/crossref_metadata_2025 huggingface dataset:
Coartinianess of local homology modules for ideals of small dimension
Authors: Shen Jingwen , Zhang Pinger , Yang Xiaoyan
Published: May 2022

Let [Formula: see text] be an ideal of a commutative noetherian ring [Formula: see text] and [Formula: see text] an [Formula: see text]-module with Cosupport in [Formula: see text]. We show that [Formula: see text] is [Formula: see text]-coartinian if and only if [Formula: see text] is artinian for all [Formula: see text], which provides finite steps to examine [Formula: see text]-coartinianess. We also consider the duality of Hartshorne’s questions: for which rings [Formula: see text] and ideals [Formula: see text] are the modules [Formula: see text] [Formula: see text]-coartinian for every artinian [Formula: see text]-module [Formula: see text] and all [Formula: see text]; whether the category [Formula: see text] of [Formula: see text]-coartinian modules is an abelian subcategory of the category of [Formula: see text]-modules, and establish affirmative answers to these questions in the case [Formula: see text] and [Formula: see text].
Cyclotomic q-Schur superalgebras
Authors: Zhao Deke
Published: May 2022

The paper aims to introduce the cyclotomic [Formula: see text]-Schur superalgebra via the permutation supermodules of the cyclotomic Hecke algebra and investigate its structure. In particular, we show that the cyclotomic [Formula: see text]-Schur superalgebra is a cellular superalgebra and establish the double centralizer property between the cyclotomic Hecke algebra and the cyclotomic [Formula: see text]-Schur superalgebra.
On non-connected pointed Hopf algebras of dimension 16 in characteristic 2
Authors: Xiong Rongchuan
Published: May 2022

Let [Formula: see text] be an algebraically closed field. We give a complete classification of non-connected pointed Hopf algebras of dimension 16 with char [Formula: see text] that are generated by group-like elements and skew-primitive elements. It turns out that there are infinitely many classes (up to isomorphism) of pointed Hopf algebras of dimension 16. In particular, we obtain infinitely many new examples of non-commutative non-cocommutative finite-dimensional pointed Hopf algebras.
New examples of indecomposable torsion-free abelian groups of finite rank and rings on them
Authors: Andruszkiewicz Ryszard R. , Woronowicz Mateusz
Published: May 2022

The paper deals with new specific constructions of indecomposable torsion-free abelian groups of rank two and nonzero rings on them. They illustrate purely theoretical results and complement quite rare examples obtained during the classical as well as recent research of additive groups of rings. The presented results concerning the homogeneous groups remain true for groups of any finite nonzero rank. Moreover, the paper contains a construction of a torsion-free indecomposable abelian group of an arbitrary finite rank greater than two supporting an associative, but not commutative ring, as well as a ring which is neither associative nor commutative.
Innovation Environment’s Role in Supporting Industry 4.0 Technology Adoption to Address Effects of COVID-19
Authors: Kruger Sean , Steyn Adriana A.
Published: May 2022

Health systems were severely strained at the start of the COVID-19 pandemic, where the demand for personal protection equipment (PPE) could not be met. The challenge faced by many countries was how to innovate quickly to create PPE and other needed solutions. The subsequent research gap identified was a lack of practical insights on how to support such novel technology adoption, particularly those that stem from Industry 4.0 (I4.0) within a developing world context. To address this previous literature on I4.0 technology, the role of innovation environments and theoretical principles of technology adoption was reviewed. A practical case from an academic makerspace based in a South African university was then assessed. It was selected due to its direct role in rapid solutions development of PPE using additive manufacturing (AM) until such a time that manufacturers could set up production on a larger scale. It was found that AM and other novel technologies have facilitated innovative solutions to address the significant impacts of the pandemic. Key to which were practices identified of an innovation environment that supported early-stage adoption of AM to achieve this even in a developing country context. The findings imply that innovation environments offer an agile platform to leverage innovation by streamlining certain critical success factors of I4.0 technology adoption, which is presented in a model. However, individual skills developed by such environments to enhance innovation capabilities within this paradigm require further research.
Bio-inspired computing with magnetic skyrmions using deep learning
Authors: Prashanth B. U. V. , Riyaz Ahmed Mohammed
Published: May 2022

Spintronic systems demonstrate significant value due to variety of features that can be used in bio-inspired computing applications. Skyrmion is a magnetic nanostructure which can be used as an information carrier. In this paper, we simulate vortex-free Gaussian wave packet by incorporating spin–orbit coupling (SoC) into three-component Bose–Einstein condensates (BECs). We next observe that skyrmions can be naturally generated from vortex-free Gaussian wave packet, further these structurally asymmetric devices are constructed and initial device classifications are carried out. A spin device consisting of vertically-stacked multiple skyrmions is proposed termed as skyrmion spintronic synapse (SkSS) and skyrmion spintronic Neuron (SkSN). This paper depicts training the neural network with surrogate gradients with deep learning strategies using topologically nontrivial spin textures. The current popular Graphics Processing Unit (GPU)-based neural network computations are compared with the proposed method. This research paves a way for a neuromorphic computing systems on exotic topological solitons.
Theoretical Model Study of Low-Energy Spin Excitations in Iron Chalcogenide AxFe2−xSe2: One Band Approach
Authors: Hota Smrutirekha , Rout G. C. , Mohanta K. L.
Published: May 2022

In this paper, we propose a one band tight binding model Hamiltonian for the newly discovered 122-type Selenide family of the iron-based system incorporated with the nearest-neighbor (NN) and the next-nearest-neighbor (NNN) hopping integrals [Formula: see text] and [Formula: see text], respectively. The spin–spin interactions of the electrons leading to the antiferromagnetic spin density wave (SDW) order have been studied at wave vector [Formula: see text]. The electron Green’s functions are calculated from the Hamiltonian model using Green’s function technique and finally the SDW gap equation is derived and studied using their NN and NNN spin coupling [Formula: see text] and [Formula: see text], respectively.
Magnetic and Thermospin Transport Properties of Triangular Graphene-Flake-Doped Boron Nitride Nanotubes
Authors: Xu Gang , Tan Xingyi , Jiang Youchang , Ren Dahua
Published: May 2022

Magnetic nanotubes are a new material platform to realize the spin-dependent Seebeck effect (SDSE) and thermal spin-filtering effect (TSFE) in spin caloritronics. Here, we designed several magnetic nanotubes using triangular graphene-flake-doped boron nitride nanotubes (GFBNNTs) and found that the band structure of these GFBNNTs can be effectively engineered by embedding graphene flakes (GFs) of different sizes. The magnetic boron nitride nanotubes (BNNTs) ((5, 5)-6N) generate a good SDSE with nearly symmetric spin-up and spin-down currents, while the magnetic BNNTs ((10, 0)-6N) generate a good TSFE. These theoretical results about the SDSE and TSFE in nanotubes enrich the spin caloritronics and suggest material candidates to realize the SDSE and other inspiring thermospin phenomena.
A Preliminary Plan to Quickly Restore Utility to the Arecibo 305m Telescope
Authors: Ferguson Dale C. , Breakall James K. , Bernhardt Paul A. , Fernandez Felix , Brum Christiano , Santoni-Ruiz Alfredo III , Kerr Robert B. , Williams Robert L.
Published: May 2022

Since the fall of the Arecibo 305[Formula: see text]m telescope platform on 2020 December 1, there has been much discussion of building a new Arecibo telescope or rebuilding the facility that was destroyed. In the collapse, the top of the three platform support towers was sheared off, and the feed arm fell free from the swinging platform and destroyed 25% of the dish. Fortunately, the Control Building, the home of the computers, spectrometers, masers, atomic clocks, 430[Formula: see text]MHz Klystrons, etc., was spared. By replacing the main dish support cables and resurfacing with coarse mesh, the main dish can be repaired as a reflector [Formula: see text] operation. By replacing the damaged aluminum panels to make the dish whole again, RF operations at up to 500[Formula: see text]MHz can be resumed. In this paper, we outline the steps that can be taken to restore High Frequency (HF, 3–30[Formula: see text]MHz) ionospheric heating, 430[Formula: see text]MHz ionospheric incoherent scattering radar, passive radio observations of satellite arcing and pulsars, and to extend the field of regard to 47∘ from the zenith. This would restore and improve much of the utility of the Arecibo dish. Part of this plan involves supporting, positioning and pointing novel point feeds from lightweight football-camera-like cables, strung from the rebuilt tower tops. It is believed that the dish may thus become broadly useful long before replacement facilities can be engineered and constructed.
Local Oscillator Concept for the Event Horizon Imager
Authors: Martín-Neira Manuel , Kudriashov Volodymyr
Published: May 2022

The Event Horizon Imager (EHI) is a mission concept conceived to provide a radio image of the surroundings of the event horizons of SgrA* and M87* supermassive black holes with an angular resolution at least an order of magnitude better than that achievable by radio telescopes on ground. The concept is based on performing space-to-space very long baseline interferometry (VLBI) using two satellites in medium Earth orbit at slightly different altitudes. Projected baselines up to about 26,000[Formula: see text]km and down to a few tens of kilometers can be realized. Free of atmospheric perturbations, EHI is observed in three channels: 557[Formula: see text]GHz, the main frequency to achieve the finest angular resolution by minimizing the scattering of the inter-stellar medium towards our galactic center; 230[Formula: see text]GHz, inherited from the event horizon telescope (EHT); finally, 43[Formula: see text]GHz in support of the relative delay determination. The whole EHI concept relies on very precise relative positioning between the two satellites including wavelength bootstrapping and a novel local oscillator concept that is the topic of this paper. It is shown that atomic clocks cannot provide sufficient coherence over the needed integration time. Instead, a system consisting of conventional crystal oscillators and an inter-satellite link (ISL) that can work coherently for extended periods of time, longer than required, and is therefore better suited for EHI is demonstrated.
SeBPPI: A Sequence-Based Protein–Protein Binding Predictor
Authors: Wang Bo , Mao Jun , Wei Min , Qi Yifei , Zhang John Z. H.
Published: May 2022

Protein–protein interaction (PPI) plays an important role in biological processes such as signal transduction, immune response and membrane transport, etc. In this work, a protein sequence-based machine learning model, SeBPPI, to predict protein–protein binding was proposed. In this model, the descriptors were generated from three pre-trained models: Unirep, ESM and TAPE. The performance of SeBPPI with these pre-trained models was evaluated on several different test datasets. The accuracy of our binary prediction model shows improvement over the existing methods. We also compared the performance of two classification heads: The Recurrent convolution neural network (RCNN) and the fully connected neural network (FNN) and found that the use of RCNN is beneficial for the overall improvement in the accuracy of the model. This study helps to improve the accuracy in sequence-based protein–protein binding predictions. The model used in this work is integrated in the web server http://www.icdrug.com/ICDrug/SeBPPI .
Exploiting Attention-Consistency Loss For Spatial-Temporal Stream Action Recognition
Authors: Xu Haotian , Jin Xiaobo , Wang Qiufeng , Hussain Amir , Huang Kaizhu
Published: May 2022

Currently, many action recognition methods mostly consider the information from spatial streams. We propose a new perspective inspired by the human visual system to combine both spatial and temporal streams to measure their attention consistency. Specifically, a branch-independent
convolutional neural network (CNN)
based algorithm is developed with a novel attention-consistency loss metric, enabling the temporal stream to concentrate on consistent discriminative regions with the spatial stream in the same period. The consistency loss is further combined with the cross-entropy loss to enhance the visual attention consistency. We evaluate the proposed method for action recognition on two benchmark datasets: Kinetics400 and UCF101. Despite its apparent simplicity, our proposed framework with the attention consistency achieves better performance than most of the two-stream networks, i.e., 75.7% top-1 accuracy on Kinetics400 and 95.7% on UCF101, while reducing 7.1% computational cost compared with our baseline. Particularly, our proposed method can attain remarkable improvements on complex action classes, showing that our proposed network can act as a potential benchmark to handle complicated scenarios in industry 4.0 applications.
An Overview of 3GPP Positioning Standards
Authors: Yang Chao , Mao Shiwen , Wang Xuyu
Published: May 2022

With the growing demand for locating services in a variety of commercial applications, positioning techniques have been considered as a vital part in cellular networks. With the evolution from 2G to 5G, the positioning techniques have been enhanced in various aspects. In this paper, we summarize the evolution of positioning standards in the Third Generation Partnership Project (3GPP) and briefly introduce the new positioning standards in 5G NR (New Radio), which include new positioning requirements, the general positioning structure, new positioning reference signals, and general positioning methods.
Platform Governance
Authors: Das Mithun , Dash Abhisek , Jaiswal Siddharth , Mathew Binny , Saha Punyajoy , Mukerjee Animesh
Published: May 2022

Platform - benign as it seems, defining the term succinctly is easier said than done [6]. It may range from an offline shopping mall to a tech-driven e-commerce platform (e.g., Amazon, Walmart, Alibaba); from a forum of public speech to an online social network (e.g., Facebook, Instagram, Twitter); from a classroom to an online encyclopedia (e.g., Wikipedia). The definition of platform varies across contexts and over time. For the purpose of this article, platforms may be understood as the online sites and services that host, organize and circulate contents and goods by facilitating social interactions among its stakeholders [5]. These interactions can range from as simple as chats, sharing and liking contents to as complicated as business transactions between stakeholders.
BioFace-3D
Authors: Wu Yi , Kakaraparthi Vimal , Li Zhuohang , Pham Tien , Liu Jian , Nguyen VP
Published: May 2022

Over the last decade, facial landmark tracking and 3D reconstruction have gained considerable attention due to their numerous applications, such as human-computer interactions, facial expression analysis, emotion recognition, etc. However, existing camera-based solutions require users to be confined to a particular location and face a camera at all times without occlusions, which largely limits their usage in practice. To overcome these limitations, we propose the first single-earpiece lightweight biosensing system, Bioface-3D, that can unobtrusively, continuously, and reliably sense the entire facial movements, track 2D facial landmarks, and further render 3D facial animations. Without requiring a camera positioned in front of the user, this paradigm shift from visual sensing to biosensing would introduce new opportunities in many emerging mobile and IoT applications.
Are WiFi Backscatter Systems Ready for the Real World?
Authors: Dehbashi Farzan , Abedi Ali , Brecht Tim , Abari Omid
Published: May 2022

WiFi backscatter communication has been proposed to enable battery-free sensors to transmit data using WiFi networks. The main advantage of WiFi backscatter technologies over RFID is that data from their tags can be read using existing WiFi infrastructures instead of specialized readers. This can potentially reduce the complexity and cost of deploying battery-free sensors. Despite extensive work in this area, none of the existing systems are in widespread use today. We hypothesize that this is because WiFi-based backscatter tags do not scale well, and their range and capabilities are limited when compared with RFID. To test this hypothesis, we conduct several real-world experiments. We compare WiFi backscatter and RFID technologies in terms of energy consumption, throughput, range and scalability.
Gibberellic Acid Production from Corn Cob Residues via Fermentation with Aspergillus niger
Authors: Monrroy Mariel , García José Renán
Published: May 2022

Following numerous biotechnological innovations, a variety of agricultural by-products can now be employed as low-cost substrates for the production of secondary metabolites, such as antibiotics, phytohormones, biofuels, pesticides, and organic acids. As an example, gibberellin (GA) growth phytohormones can be obtained by such means, wherein gibberellic acid (GA3) is of great interest worldwide in the agricultural sector. The central aspect of this research therefore focused on the bioconversion of agricultural by-products, such as corn cob, to obtain GA3 phytohormone via solid-state fermentation (SSF) with Aspergillus niger. The chemical characterization of the obtained material showed that the corn cob possessed glucose, mannose, arabinose, and lignin contents of 34, 26, 8, and 16%, respectively. Our results also indicated an appreciable carbon content (47%), in addition to the mineral elements of nitrogen (4%), potassium (1.2%), iron (0.03%), sodium (0.01%), calcium (0.06%), and Al (0.02%). Following SSF for 11 d in the presence of A. niger at pH 5, 30°C, and 24% sample consistency, a GA3 production of >6.1 g·kg−1 was obtained. This value is higher than those previously reported for different by-products of the food industry, such as coffee husk, wheat bran, cassava, pea pods, and sorghum straw (i.e., 0.25–5.5 g·kg−1) following SSF. The production of GA3 from corn cob residues not only contributes to reducing the negative impact of agricultural by-products but also represents a new source of a key raw material for phytohormone production, thereby contributing to the development of processes to convert agricultural residues into biologically active compounds of commercial interest.
Analysis of Settlement Induced by Shield Construction of the Metro Passing under Existing Buildings Based on the Finite Difference Method
Authors: Wang Rui , Zhang Bin , Wang You
Published: May 2022

A frequent problem when constructing metro tunnels is the uneven settlement of adjacent buildings, which results in huge economic losses. Due to shields causing the settlement of adjacent buildings, this study took the Nantong Metro Line 1 as the research object. The metro runs from the East Huancheng Road Station to the Nantong Intermediate People’s Court Station and passes under the old residential area of the Community Sendadi-Huayuan building. Based on the settlement monitoring data, this study analyzed the impact of tunnel shield construction on the foundation settlement of adjacent buildings. The computational analysis of the shield tunnel was performed by numerical simulation, taking into account the upper building load and the construction disturbance to the surrounding rock. Afterward, the study summarized the law behind the settlement. The results show that when the boring machine was tunneling through sandy soil and silt rich in water, its head was prone to twisting due to soil disturbance and the dissipation of pore water, causing instability in the excavation process and eventual ground settlement. In the sandy strata, the relationship between foundation settlement caused by shield construction and time can be approximated as an exponential function in a certain area. The vibrations from shield excavation caused the foundations to vibrate for a short period of time. Therefore, the average settlement of buildings directly above the underpass reached 7 mm/d. Likewise, the tunneling shield caused a 1.5 mm settlement before passing under the building. It caused a peak settlement of 4.0 and 5.1 mm during and after passing under the buildings, respectively. The width of the settlement troughs of approximately 7D formed after the tunnel was excavated. Shield excavation placed the soil at the top of the excavation face in a state of tensile stress, causing tensile and shear damage to the soil and forming a local plastic zone.
Intrusion Detection Model for Industrial Internet of Things Based on Improved Autoencoder
Authors: Zhang Wumei , Zhang Yongzhen
Published: May 2022

With the gradual advancement of informatization and industrialization, the safety and controllability of industrial Internet of things (IoT) have attracted more and more attention. Aiming to improve the security of industrial IoT, a detection method using stacked sparse autoencoder network model is proposed. In this method, the basic units of the network model have been simplified and sparse, and some of basic features are combined with obtaining a higher-level abstract expression, so as to solve the problem of unbalanced network traffic data. The cascaded network structure is adopted to stack its sparse autoencoder network model, so as to improve the data ability of the detection model. In addition, the incorporation of Softmax classifier realizes the dynamic adjustment and optimization of the whole network parameters, which further ensures the efficiency of the detection method. The simulation experiment is based on NSL-KDD dataset. The experiment has proved that the proposed method has excellent network attack identification and detection performance. Its accuracy index is about 95.42%, and the detection time is about 3.42 s.
Research on Semisupervised Affective Interaction Technology in Immersive Virtual Teaching Environment
Authors: Tang Wenzhe , Zhang Fang
Published: May 2022

The separation of time and space in immersive virtual teaching makes students unable to realize emotional communication, which may affect students’ mental health. In recent years, the use of affective computing technology to solve the problem of affective loss in distance education has become a key research topic. In order to realize the problem of emotion interaction in immersive virtual teaching, a semisupervised support vector machine- (SVM-) based affective interaction model was proposed. First, the natural language sequences of students in the virtual teaching environment are preprocessed using a statistical-based framing method, and mutual information and expected cross-entropy are used as feature selection methods. Then, a vector space model based on TF/IDF feature term weights is proposed to implement the feature vector representation of natural language sequences. Finally, after the constructed sentiment space, a semisupervised SVM is employed as the classifier to complete the affective interaction computation. The experimental results of emotion classification show that the proposed model is able to determine and understand the emotional state more accurately than other traditional models and significantly improves the training speed. In addition, the proposed model can provide emotional encouragement or emotional compensation according to the specific emotional state of the learner.
The Effect of Renal Denervation on Cardiac Diastolic Function in Patients with Hypertension and Paroxysmal Atrial Fibrillation
Authors: Xu Hai , Jiang Zhixin , Jiang Wanying , Huo Junyu , Shan Qijun
Published: May 2022

Objective. Renal artery denervation (RDN) can treat hypertension and paroxysmal atrial fibrillation (PAF). Hypertension and PAF can affect cardiac diastolic function. The study aimed to evaluate the effect of RDN on cardiac diastolic function in patients with refractory hypertension and PAF. Methods. 190 consecutive patients with hypertension and PAF were recruited. The levels of NT-proBNP and metrics of echocardiography were measured before and after RDN in patients with refractory hypertension and PAF. The 190 patients were divided into the decreasing HR and nondecreasing HR group, the decreasing MAP and nondecreasing MAP group, the HFPEF group, and the normal diastolic function group, respectively. Results. Before RDN, the indices about cardiac diastolic function were out of the normal range. After RDN, the diastolic function improved in the indices of NT-proBNP, E/e′, e′. The diastolic function about the indices of NT-proBNP, E/e′, e′ was improved in the decreasing HR group, the decreasing mean arterial pressure (MAP) group, and the HFPEF group, correspondingly compared to the nondecreasing HR group, the non-decreasing MAP group, and the preoperative normal diastolic function group. In the multivariate analysis, the MAP and HR were the only two indicators significantly associated with the improvement of diastolic function. Conclusion. RDN could improve the diastolic function in patients with refractory hypertension and PAF. Patients with HFPEF could receive benefits through RDN. It was speculated that RDN improved the diastolic function mainly through decreasing HR and MAP.
Experimental and Computational Study on Conductors Bearing Capacity in Offshore Drilling
Authors: Hu Nanding , Yang Jin , Suduna Bao , Wang Jiakang , Ding Yida , Liu Xun , Yu Chen
Published: May 2022

The height of the cementing cement sheath of the conductor for offshore drilling is the key factor affecting wellhead stability. For the influence of the insufficient return height of cement slurry on the mechanical behavior of the conductor after running the conductor by shallow water exploration well drilling, the axial load distribution characteristics and bearing capacity mechanism of conductor were first analyzed from the perspective of operation characteristics of the drilling method. Second, a calculation model of axial bearing capacity of the conductor with the size of the conductor and the return height of cement slurry as variables was established on the basis of the hole enlargement that often occurs during drilling. In addition, the law of the influence of the return height of cement slurry on the ultimate bearing capacity of conductor and the strength difference of the two cementing surfaces between conductor and cement ring and between the cement sheath and submarine soil layer and its changing rules with time were explored and studied by field experiment. It was concluded that the friction between the cement sheath and the soil layer is the key factor that decides the main bearing capacity of the conductor.
Parallel Bookkeeping Path of Accounting in Government Accounting System Based on Deep Neural Network
Authors: Li Qing
Published: May 2022

“Parallel bookkeeping” is a key technical arrangement to achieve the goal of moderately separating and connecting the financial accounting system and budget accounting system established by the government accounting system. It is still a new thing for the majority of financial personnel in the government accounting subject. A deep neural network is the basis of deep learning. Up to now, the neural network has been applied in many fields, and its application in the financial field is more in-depth. The neural network is of great help to financial accounting. Integrating it into parallel bookkeeping in accounting can improve the work efficiency and accuracy of financial personnel. Through experimental analysis, it is found that its efficiency and accuracy are improved by 45% and 21.34% compared with the previous parallel bookkeeping path. The accounting parallel bookkeeping path based on the deep neural network studied in this paper not only has great practical significance for the work of financial personnel but also has far-reaching significance for the research of accounting paths in the future.
The Research of Chinese Martial Arts Cross-Media Communication System Based on Deep Neural Network
Authors: Su Yue , Tian Jing , Zan Xin
Published: May 2022

The spread of Chinese martial arts is crucial for the world to understand Chinese culture. If only relying on one transmission method, it will lead to the difference of transmission and its lack of certain real time. This will lead to differences in the understanding of Chinese martial arts, which is also not conducive to the spread of Chinese glorious culture. Cross-media communication technology can solve this communication difference problem very well. The deep neural network method was used to fuse relevant features of Chinese martial arts, and it also analyzes the feasibility of neural network technology in cross-media communication. At the same time, this study uses deep neural network to study the timeliness of Chinese martial arts in the process of cross-media communication. The research results show that the convolutional neural network can effectively extract the characteristics of Chinese martial arts and carry out effective dissemination. However, the hybrid convolutional neural network with temporal features has higher accuracy in extracting Chinese martial arts features. This hybrid convolutional neural network is more conducive to the dissemination of Chinese martial arts through cross-media technology, which can ensure its timeliness. The maximum error of deep neural network technology in predicting Chinese martial arts culture is only 2.67%. This part of the error comes from the action characteristics of Chinese martial arts culture, which shows that neural network technology has good feasibility.
An Enhanced Posture Prediction-Bayesian Network Algorithm for Sleep Posture Recognition in Wireless Body Area Networks
Authors: Roshini A. , Kiran K. V. D.
Published: May 2022

Wireless body area networks have taken their unique recognition in providing consistent facilities in health monitoring. Several studies influence physiological signal monitoring through a centralized approach using star topology in regular activities like standing, walking, sitting, and running which are considered active postures. Unlike regular activities like walking, standing, sitting, and running, the in-bed sleep posture monitoring of a subject is highly necessary for those who have undergone surgery, victims of breathing problems, and victims of COVID-19 for whom oxygen imbalance is a major issue as the mortality rate in sleep is high due to unattended patients. Suggestions from the medical field state that the patients with the above-mentioned issues are highly suggested to follow the prone sleep posture that enables them to maintain the oxygen level in the human body. A distributed model of communication is used where mesh topology is used for the data packets to be carried in a relay fashion to the sink. Heartbeat rate (HBR) and image monitoring of the subject during sleep are closely monitored and taken as input to the proposed posture prediction-Bayesian network (PP-BN) to predict the consecutive postures to increase the accuracy rate of posture recognition. The accuracy rate of the model outperforms the existing classification and prediction algorithms which take the cleaned dataset as input for better prediction results.
Multimodal English Teaching Classroom Interaction Based on Artificial Neural Network
Authors: Hua Wenbin
Published: May 2022

In recent years, with the rapid development of science and technology, traditional teaching methods and concepts have been frequently impacted. Artificial neural network shows excellent intelligence because of its powerful nonlinear processing ability and efficient associative function. It is increasingly becoming an emerging object in the field of artificial intelligence. At the same time, in the field of education and teaching, the integration of English teaching and multimodality not only condenses the characteristics of the times but also expands new teaching models, bringing opportunities for the emergence of new teaching models. Based on this, this study proposes an interactive method for multimodal English teaching based on artificial neural networks. It aims to study how to use the autonomous learning of artificial neural networks to accelerate the fusion of different modalities and at the same time make suggestions for different teaching interaction modes. This paper firstly analyzes the interaction of English teaching under the traditional mode. It then proposes a multimodal fusion interaction method based on artificial neural networks. It finally explores the feasibility of the new interaction theory by setting up an experimental group and a control group. Through the analysis of the experimental data, the final data results show that the multimodal fusion interaction based on artificial neural network has a very significant effect, and the students' interest in the English classroom is as high as 81.9%. This fully demonstrates the great value of the new fusion method, and it has certain enlightening significance for the establishment of English teaching modes and curriculum reform.
Lung Cancer Detection Based on Kernel PCA-Convolution Neural Network Feature Extraction and Classification by Fast Deep Belief Neural Network in Disease Management Using Multimedia Data Sources
Authors: Jain Deepak Kumar , Lakshmi Kesana Mohana , Varma Kothapalli Phani , Ramachandran Manikandan , Bharati Subrato
Published: May 2022

In lung cancer, tumor histology is a significant predictor of treatment response and prognosis. Although tissue samples for pathologist view are the most pertinent approach for histology classification, current advances in DL for medical image analysis point to the importance of radiologic data in further characterization of disease characteristics as well as risk stratification. Cancer is a complex global health problem that has seen an increase in death rates in recent years. Progress in cancer disease detection based on subset traits has enabled awareness of significant as well as exact disease diagnosis, thanks to the rapid flowering of high-throughput technology as well as numerous ML techniques that have emerged in recent years. As a result, advanced ML approaches that can successfully distinguish lung cancer patients from healthy people are of major importance. This paper proposed lung tumor detection based on histopathological image analysis using deep learning architectures. Here, the input image is taken as a histopathological image, and it has also been processed for removing noise, image resizing, and enhancing the image. Then the image features are extracted using Kernel PCA integrated with a convolutional neural network (KPCA-CNN), in which KPCA has been used in the feature extraction layer of CNN. The classification of extracted features has been put into effect using a Fast Deep Belief Neural Network (FDBNN). Finally, the classified output will give the tumorous cell and nontumorous cell of the lung from the input histopathological image. The experimental analysis has been carried out for various histopathological image datasets, and the obtained parameters are accuracy, precision, recall, and F-measure. Confusion matrix gives the actual class and predicted class of tumor in an input image. From the comparative analysis, the proposed technique obtains enhanced output in detecting the tumor once compared with an existing methodology for the various datasets.
Feature Extraction of Ship-Radiated Noise Based on Hierarchical Dispersion Entropy
Authors: Xiao Leilei
Published: May 2022

The classification and recognition of ship-radiated noise (SRN) is of great significance to the processing of underwater acoustic signals. In order to improve the stability of recognition and more accurately identify SRN, single feature extraction and dual feature extraction based on hierarchical dispersion entropy (HDE) are proposed. For single feature extraction, HDE of the best node among the eight nodes of the third layer decomposition is extracted. For dual feature extraction, HDE of the best two nodes among the 14 nodes of the first-, second-, and third-layer decompositions are required. The results show that the recognition rate of single and dual feature extraction originated from the method based on HDE reaches 85% and 100%, respectively, better than the method of hierarchical reverse dispersion entropy (HRDE) and hierarchical permutation entropy (HPE).
The Incidence-Based Mortality and Survival Trends in Patients with Gastric Signet Ring Cell Carcinoma
Authors: Li Xiangpan , Chu Yuxin , Song Qibin , Hu Qinyong
Published: May 2022

Background and Aim. Gastric signet ring cell carcinoma (SRC) is a distinct subtype of gastric cancer. This study aims to analyze the incidence-based mortality and survival trends in patients with gastric SRC over the past decades. Materials and Methods. The patients from the Surveillance, Epidemiology, and End Results (SEER) database were recruited to explore the incidence-based mortality and survival trends from 2000 to 2017. We further analyzed the differences in mortality and survival trends in these patients by sex and stage. We also used joinpoint software to evaluate the trends in annual percentage change (APC) for statistical significance. Results. 14916 patients were collected, including 7801 (52.3%) male and 7115 (47.7%) female. We identified a single joinpoint at 2002. The overall incidence-based mortality of gastric SRC declined in America after 2002 (APC = −1.21,

P
<
0.05

). In stratified analysis by sex and stage, the incidence-based mortality rate was higher in males than females. After 2002, the mortality rate decreased significantly in male (APC = −1.68,

P
<
0.05

) and M0-stage patients (APC = −1.75,

P
<
0.05

). In survival trend analysis, the 2-year relative survival improved in M0-stage gastric SRC, especially for males (APC = 1.14,

P
<
0.05

). As for M1-stage patients, the 2-year relative survival significantly elevated in both male (APC = 3.87,

P
<
0.05

) and female (APC = 5.63,

P
<
0.05

) patients. Conclusions. The incidence-based mortality of gastric SRC has declined, and survival has improved in America over time. These optimistic trends may be attributed to cancer screening implementation and advances in novel treatments in the past decades.
The Ordering of the Unicyclic Graphs with respect to Largest Matching Root with Given Matching Number
Authors: Gong Luozhong , Liu Weijun
Published: May 2022

The matching roots of a simple connected graph G are the roots of the matching polynomial which is defined as , where m(G, k) is the number of the k matchings of G. Let λ1(G) denote the largest matching root of the graph G. In this paper, among the unicyclic graphs of order n, we present the ordering of the unicyclic graphs with matching number 2 according to the λ1(G) values for n ≥ 11 and also determine the graphs with the first and second largest λ1(G) values with matching number 3.
The Development of “Internet +” Smart Environmental Protection Multi-Environment Perception System
Authors: Xu Zuomei
Published: May 2022

With the development of the economy and people’s longing for a better living environment, the smart and environmentally friendly ecological environment has entered a period of intensive research. However, the current ecological environment resources cannot be used reasonably, and word of mouth and tourism economy are showing negative growth. In order to be able to conduct an in-depth study of the ecological environment, based on “Internet +” and perceived value, this paper builds a comprehensive evaluation model of a smart and environmentally friendly ecological environment based on a multiple perception system. This article establishes an evaluation index system that combines two systems of multiple perception systems and ecological environment, six secondary indicators, and 22 three-level indicators. In this paper, the comprehensive evaluation value of the smart environmental protection ecological environment is 0.78595, and the level of the multi-perception system is good. At the same time, the corresponding evaluation values of visual perception, functional service perception, and cultural and activity perception are 0.75431, 0.70196, and 0.78846, respectively. The comprehensive evaluation value of the ecological environment is 0.65734, and the level of ecological environment development is average. The corresponding evaluation values of the subsystem environmental quality level, environmental pressure level, and environmental improvement level are 0.69943, 0.63491, and 0.60279, respectively. The coordination value of the multi-perception system and the ecological environment is 0.9978, which is an ultra-high-level coordination stage. The comprehensive evaluation value of the two is 0.8659. At this stage, the degree of interaction between the two is strong and affects their development.
Research Trends around Exercise Rehabilitation among Cancer Patients: A Bibliometrics and Visualized Knowledge Graph Analysis
Authors: Pan Yun , Deng Xianyu , Zhuang Ying , Li Jiyu
Published: May 2022

This study analyzed the research hotspots and frontiers of exercise rehabilitation among cancer patients via CiteSpace. Relevant literature published in the core collection of the Web of Science (WoS) database from January 1, 2000, to February 6, 2022, was searched. Further, we used CiteSpace5.8R1 to generate a network map and identified top authors, institutions, countries, keywords, and research trends. A total of 2706 related literature were retrieved. The most prolific writer was found to be Kathryn H Schmitz (21 articles). The University of Toronto (64 articles) was found to be the leading institution, with the United States being the leading country. Further, “rehabilitation,” “exercise,” “quality of life,” “cancer,” and “physical activity” were the top 5 keywords based on frequency; next, “disability,” “survival,” “fatigue,” “cancer,” and “rehabilitation” were the top 5 keywords based on centrality. The keyword “fatigue” was ranked at the top of the most cited list. Finally, “rehabilitation medicine,” “activities of daily living,” “lung neoplasm,” “implementation,” “hospice,” “exercise oncology,” “mental health,” “telemedicine,” and “multidisciplinary” are potential topics for future research. Our results show that the research hotspots have changed from “quality of life,” “survival,” “rehabilitation,” “exercise,” “cancer,” “physical therapy,” “fatigue,” and “breast cancer” to “exercise oncology,” “COVID‐19,” “rehabilitation medicine,” “inpatient rehabilitation,” “implementation,” “telemedicine,” “lung neoplasm,” “telehealth,” “multidisciplinary,” “psycho‐oncology,” “hospice,” “adapted physical activity,” “cancer‐related symptom,” “cognitive function,” and “behavior maintenance.” Future research should explore the recommended dosage and intensity of exercise in cancer patients. Further, following promotion of the concept of multidisciplinary cooperation and the rapid development of Internet medical care, a large amount of patient data has been accumulated; thus, how to effectively use this data to generate results of high clinical value is a question for future researchers.
Wind Energy Resource Prediction and Optimal Storage Sizing to Guarantee Dispatchability: A Case Study in the Kenyan Power Grid
Authors: Odero Hampfrey , Wekesa Cyrus , Irungu George
Published: May 2022

Kenya is experiencing a fast increase in grid-connected intermittent renewable energy sources (RESs) to meet its increased power demand, and at the same time be able to fulfill its Paris Agreement obligations of abating greenhouse gas emissions. For instance, Kenya has 102 MW of grid-tied solar power and 410 MW of grid-tied wind power. However, these sources are very intermittent with low predictability. Thus, after their installation and integration into the grid, they impose a new challenge for the secure, reliable, and economic operation of the system. To mitigate these and to ensure proper planning of the system operations, accurate and faster prediction of the generation output of the wind energy resources and optimal design and sizing of storage for the large-scale wind energy integration into the grid are of paramount importance. Artificial intelligence (AI) and metaheuristic techniques have proven to be efficient and robust in offering solutions to complex nonlinear prediction and optimization problems. Therefore, this study aims to utilize backpropagation neural network (BPNN) algorithm to conduct hourly prediction of the generation output of Lake Turkana Wind Power Plant (LTWPP), a 310 MW plant connected to the Kenyan power grid, and optimally size its battery energy storage system (BESS) using genetic algorithm (GA) to guarantee its dispatchability. The historical weather data, namely wind speed, ambient temperature, relative humidity, wind direction, and generation output from LTWPP, are employed in the training, testing, and validation of the neural network. LTWPP and BESS are modelled in MATLAB R2016a software. Thereafter, the developed BPNN and GA algorithms are applied to the modelled systems to predict the wind output and optimize the storage system, respectively. BESS optimization with neural prediction reduces the BESS capacity and investment costs by 59.82%, while the overall dispatchability of LTWPP is increased from 73.36% to 90.14%, hence enabling the farm to meet its allowable loss of power supply probability (LPSP) index of 0.1 while guaranteeing its dispatchability.
On the Elementary Symmetric Polynomials and the Zeros of Legendre Polynomials
Authors: Alatawi Maryam Salem
Published: May 2022

In this paper, we seek to present some new identities for the elementary symmetric polynomials and use these identities to construct new explicit formulas for the Legendre polynomials. First, we shed light on the variable nature of elementary symmetric polynomials in terms of repetition and additive inverse by listing the results related to these. Sequentially, we have proven an important formula for the Legendre polynomials, in which the exponent moves one walk instead of twice as known. The importance of this formula appears throughout presenting Vieta’s formula for the Legendre polynomials in terms of their zeros and the results mentioned therein. We provide new identities for the elementary symmetric polynomials of the zeros of the Legendre polynomials. Finally, we propose the relationship between elementary symmetric polynomials for the zeros of Pn(x) and the zeros of Pn−1(x).