bluuebunny/crossref_metadata_2025 huggingface dataset:
The quest for negative methane: The CH4− anion
Authors: Ramírez-Solís Alejandro , Hinojosa Guillermo , Saint-Martin Humberto
Published: July 2022

In this review, we present the recent discovery and confirmation of a new negative molecular ion, the [Formula: see text] anion. The experimental identification of this high-spin exciplex was difficult because it overlaps with the negative oxygen ion commonly present as a contaminant in vacuum systems and with the same mass-spectrometric signature. Born–Oppenheimer molecular dynamics (BOMD) simulations finally reveal that this anion is a quartet ([Formula: see text]) metastable species, which leads to the formation of a molecular ([Formula: see text] excited complex.
New unexpected soliton solutions to the generalized (2 + 1) Schrödinger equation with its four-mixing waves
Authors: Zahran Emad H. M. , Bekir Ahmet
Published: July 2022

The propagation of solitons in birefringent fiber is one of the phenomena that has an important role in all modern technological means of communication. The generalized [Formula: see text] nonlinear Schrödinger equation (GNLSE) with its four-mixing waves (FMW) is the famous model that describes the propagation of solitons in birefringent fiber perfectly. In fact, the FMW governed effectively the performance of the resultant solitons amplitude. Hereby, we will study this model to extract new unexpected optical soliton solutions to this model via various three techniques. The three famous methods that are a candidate for this purpose are the extended direct algebraic method (EDAM), the extended simple equation method (ESEM) and the solitary wave ansatz method (SWAM). The three techniques are implemented successively for the suggested model to establish the optical solutions of the suggested model successfully. The optical soliton solutions that are achieved by these proposed techniques give surprise expectations that weren’t achieved previously by any other authors who used other techniques.
Electric current and magnetization distributions’ self-organization features in a magnetoresistive film nanoelement under the influence of an external magnetic field
Authors: Shevtsov V. S. , Polyakov P. A.
Published: July 2022

The new generalized Laplace’s equation for an electric potential is presented, which in the general case, is nonlinear for media with resistance anisotropy caused by the anisotropic magnetoresistance (AMR) effect and determined by the orientation of the magnetization vector relative to the current density vector at an arbitrary point of the sample. The solution to this equation is obtained for an anisotropic medium with a non-uniform distribution of current density and magnetization in the case of an oblique-shaped plate of nanoscale thickness. In addition, the analytical solution was obtained for the current density distribution in such a medium using conformal mapping, which confirms the numerical solution in the case of an isotropic conductor. An analysis of the results showed that the presence of the AMR effect leads to a significant change in the current density distribution in the sample compared to the isotropic case, which is expressed in the deviation of the current density vector in perpendicular to the magnetization vector direction in the plate. It has been established that the presence of an external magnetic field in the film plane, which leads to the emergence of an inhomogeneous magnetization distribution, entails self-organization of the electric current distribution. The graphs of the plate resistance as a function of applied magnetic field demonstrate asymmetry for the cases of the direct and reverse directions of the external magnetic field, which cannot be obtained within the framework of simplified models of current flow in media with the AMR effect.
An Investigation on the Impact of Technological Enablement on the Success of Entrepreneurial Adoption Among Higher Education Students: A Comparative Study
Authors: Keoy Kay Hooi , Thong Chee Ling , Cherukuri Aswani Kumar , Koh Yung Jing , Chit Su Mon , Lee Luqman , Genaro Japos , Kwek Choon Ling
Published: July 2022

Technology greatly supports people’s daily lives such as education, business, medical, and many other aspects. It can be noted that the higher education institutions’ students rely on technological support and university assistance for their studies during the COVID-19 pandemic. Technological enablement is the primary determinant for entrepreneurial initiation that received attention from scholars. The focus areas include how governmental support, entrepreneurial intention, entrepreneurial education and technological enablement (mediator factor) can influence the entrepreneurial initiation. Empirical studies showed the direct and indirect impacts of the contributing factors in a particular area. However, is it the same effect of the factors for different countries? This study conducted a self-administered questionnaire to collect topic-related information from higher education institutions in Malaysia and the Philippines. A formative-reflective model, PLS-MGA, was used to analyse the direct and indirect impacts alongside the mediating factor, technological enablement. The results showed that entrepreneurial competencies, Entrepreneurial Education System, Entrepreneurial Education Mechanism, and Entrepreneurial Intention positively and significantly impact entrepreneurial success in both regions. However, the result also demonstrated that the impact of technological enablement on entrepreneurial success is more significant in Malaysia than in the Philippines. With such findings, policymakers and institutions in both countries can understand the insight and importance of technological enablement in stimulating entrepreneurship and its perceived success. Hence, they can implement supportive strategies and necessary policies to ensure technology adoption, success in shaping students’ entrepreneurial mindset and achieving the perceived outcome.
Leveraging Mobile Sensing and Bayesian Change Point Analysis to Monitor Community-scale Behavioral Interventions: A Case Study on COVID-19
Authors: Kumar Shashwat , Datta Debajyoti , Dong Guimin , Cai Lihua , Boukhechba Mehdi , Barnes Laura
Published: July 2022

During pandemics, effective interventions require monitoring the problem at different scales and understanding the various tradeoffs between efficacy, privacy, and economic burden. To address these challenges, we propose a framework where we perform Bayesian change-point analysis on aggregate behavior markers extracted from mobile sensing data collected during the COVID-19 pandemic. Results generated by 598 participants for up to four months reveal rich insights: We observe an increase in smartphone usage around February 10th, followed by an increase in email usage around February 27th and, finally, a large reduction in participant’s mobility around March 13th. These behavior changes overlapped with important news events and government directives such as the naming of COVID-19, a spike in the number of reported cases in Europe, and the declaration of national emergency by President Trump. We also show that our detected change points align with changes in large scale external sources, including number of COVID-19 tweets, COVID-19 search traffic, and a large-scale foot traffic data collected by SafeGraph, providing further validation of our method. Our results show promise towards the feasibility of using mobile sensing to understand communities’ responses to public health interventions.
Solving Program Sketches with Large Integer Values
Authors: Hu Qinheping , Singh Rishabh , D’Antoni Loris
Published: July 2022

Program sketching is a program synthesis paradigm in which the programmer provides a partial program with holes and assertions. The goal of the synthesizer is to automatically find integer values for the holes so that the resulting program satisfies the assertions. The most popular sketching tool,
Sketch
, can efficiently solve complex program sketches but uses an integer encoding that often performs poorly if the sketched program manipulates large integer values. In this article, we propose a new solving technique that allows
Sketch
to handle large integer values while retaining its integer encoding. Our technique uses a result from number theory, the Chinese Remainder Theorem, to rewrite program sketches to only track the remainders of certain variable values with respect to several prime numbers. We prove that our transformation is sound and the encoding of the resulting programs are exponentially more succinct than existing
Sketch
encodings. We evaluate our technique on a variety of benchmarks manipulating large integer values. Our technique provides speedups against both existing
Sketch
solvers and can solve benchmarks that existing
Sketch
solvers cannot handle.
A Scalable Many-core Overlay Architecture on an HBM2-enabled Multi-Die FPGA
Authors: Abdelhamid Riadh Ben , Yamaguchi Yoshiki , Boku Taisuke
Published: July 2022

The overlay architecture enables to raise the abstraction level of hardware design and enhances hardware-accelerated applications’ portability. In FPGAs, there is a growing awareness of the overlay structure as typified by many-core architecture. It works in theory; however, it is difficult in practice, because it is beset with serious design issues. For example, the size of FPGAs is bigger than before. It is exacerbating the issue of the place-and-route. Besides, a single FPGA is actually the sum of small-to-middle FPGAs by advancing packaging technology like silicon interposers. Thus, the tightly coupled many-core designs will face this covert issue that the wires among the regions are extremely restricted. This article proposes efficient essential processing elements, micro-architecture design, and the interconnect architecture toward a scalable many-core overlay design. In particular, our work proposes a novel compact buffering technique to reduce memory resource utilization in tightly connected overlays while preserving computational efficiency. This technique reduces the utilization of BlockRAM to nearly 50% while achieving a best-case computational efficiency of 91.93% in a three-dimensional Jacobi benchmark. Besides, the proposed enhancements led to around 2× and 3× improvement in performance and power efficiency, respectively. Moreover, the improved scalability allowed increasing compute resources and delivering around 4× better performance and power efficiency, as compared to the baseline Dynamically Re-programmable Architecture of Gather-scatter Overlay Nodes overlay.
Breaking Good: Fracture Modes for Realtime Destruction
Authors: Sellán Silvia , Luong Jack , Mattos Da Silva Leticia , Ramakrishnan Aravind , Yang Yuchuan , Jacobson Alec
Published: July 2022

Drawing a direct analogy with the well-studied vibration or elastic modes, we introduce an object’s
fracture modes
, which constitute its preferred or most natural ways of breaking. We formulate a sparsified eigenvalue problem, which we solve iteratively to obtain the
n
lowest-energy modes. These can be precomputed for a given shape to obtain a prefracture pattern that can substitute the state of the art for realtime applications at no runtime cost but significantly greater realism. Furthermore, any realtime impact can be projected onto our modes to obtain impact-dependent fracture patterns without the need for any online crack propagation simulation. We not only introduce this theoretically novel concept, but also show its fundamental and practical advantages in a diverse set of examples and contexts.
The Linear Algebra Mapping Problem. Current State of Linear Algebra Languages and Libraries
Authors: Psarras Christos , Barthels Henrik , Bientinesi Paolo
Published: July 2022

We observe a disconnect between developers and end-users of linear algebra libraries. On the one hand, developers invest significant effort in creating sophisticated numerical kernels. On the other hand, end-users are progressively less likely to go through the time consuming process of directly using said kernels; instead, languages and libraries, which offer a higher level of abstraction, are becoming increasingly popular. These languages offer mechanisms that internally map the input program to lower level kernels. Unfortunately, our experience suggests that, in terms of performance, this translation is typically suboptimal.

In this paper, we define the problem of mapping a linear algebra expression to a set of available building blocks as the“Linear Algebra Mapping Problem” (LAMP); we discuss its NP-complete nature, and investigate how effectively a benchmark of test problems is solved by popular high-level programming languages and libraries. Specifically, we consider Matlab, Octave, Julia, R, Armadillo (C++), Eigen (C++), and NumPy (Python); the benchmark is meant to test both compiler optimizations, as well as linear algebra specific optimizations, such as the optimal parenthesization of matrix products. The aim of this study is to facilitate the development of languages and libraries that support linear algebra computations.
Passive and Context-Aware In-Home Vital Signs Monitoring Using Co-Located UWB-Depth Sensor Fusion
Authors: Xie Zongxing , Zhou Bing , Cheng Xi , Schoenfeld Elinor , Ye Fan
Published: July 2022

Basic vital signs such as heart and respiratory rates (HR and RR) are essential bio-indicators. Their longitudinal in-home collection enables prediction and detection of disease onset and change, providing for earlier health intervention. In this article, we propose a robust, non-touch vital signs monitoring system using a pair of co-located Ultra-Wide Band (UWB) and depth sensors. By extensive manual examination, we identify four typical temporal and spectral signal patterns and their suitable vital sign estimators. We devise a probabilistic weighted framework (PWF) that quantifies evidence of these patterns to update the weighted combination of estimator output to track the vital signs robustly. We also design a “heatmap”-based signal quality detector to exclude the disturbed signal from inadvertent motions. To monitor multiple co-habiting subjects in-home, we build a two-branch long short-term memory (LSTM) neural network to distinguish between individuals and their activities, providing activity context crucial to disambiguating critical from normal vital sign variability. To achieve reliable context annotation, we carefully devise the feature set of the consecutive skeletal poses from the depth data, and develop a probabilistic tracking model to tackle non-line-of-sight (NLOS) cases. Our experimental results demonstrate the robustness and superior performance of the individual modules as well as the end-to-end system for passive and context-aware vital sign monitoring.
HiGAN+: Handwriting Imitation GAN with Disentangled Representations
Authors: Gan Ji , Wang Weiqiang , Leng Jiaxu , Gao Xinbo
Published: July 2022

Humans remain far better than machines at learning, where humans require fewer examples to learn new concepts and can use those concepts in richer ways. Take handwriting as an example, after learning from very limited handwriting scripts, a person can easily imagine what the handwritten texts would like with other arbitrary textual contents (even for unseen words or texts). Moreover, humans can also hallucinate to imitate calligraphic styles from just a single reference handwriting sample (that even have never seen before). Humans can do such hallucinations, perhaps because they can learn to disentangle the textual contents and calligraphic styles from handwriting images. Inspired by this, we propose a novel handwriting imitation generative adversarial network (HiGAN+) for realistic handwritten text synthesis based on disentangled representations. The proposed HiGAN+ can achieve a precise one-shot handwriting style transfer by introducing the writer-specific auxiliary loss and contextual loss, and it also attains a good global & local consistency by refining local details of synthetic handwriting images. Extensive experiments, including human evaluations, on the benchmark dataset validate our superiority in terms of visual quality, scalability, compactness, and style transferability compared with the state-of-the-art GANs for handwritten text synthesis.
Layered Double Hydroxide-Modified Electrodes for Gaseous Acetaldehyde Degradation at the Solid-Gas Interphase
Authors: Govindan Muthuraman , Yeom Dohyeon , Choi Youngyu , Kim Daekeun
Published: July 2022

Electrochemical application has been considered a promising technology in environmental remediation. However, the development of hydrocarbon ligand-free mediators for long-term operation still deserves further assessments. In this study, three different combinations (NiAl, CoAl, NiCo) of layered double hydroxide (LDH) electrodes were prepared using in situ and ex situ methods. These LDH electrodes were applied as solid electrocatalysts for gaseous acetaldehyde degradation using a membrane-divided flow-through electrolytic cell. In cyclic voltammetry analysis, the in situ prepared LDH-electrodes had high peak currents in high-valent redox couples (Ni3+/2+Al3+, Co3+/2+Al3+, and Ni3+/2+Co3+/2+) compared to low-valent redox couples (Ni2+/1+Al3+, Co2+/1+Al3+, and Ni2+/1+Co2+/1+). Due to the higher active surface area, the overall redox peak current was higher in the in situ prepared LDH electrode than the ex situ LDH electrode. During electrocatalytic degradation of acetaldehyde, the LDH electrodes containing cobalt ions had a higher mediated catalysis activity than the LDH containing nickel ions (NiAl-LDH). The Co3+ ions placed in the layered hydroxide synergistically mediate the electrons to degrade acetaldehyde at the solid-gas interface sustainably.
Anthraquinone Functionalized Ionic Liquid Based Disposable Electrochemical Sensor for the Enzyme-Free Detection of Hydrogen Peroxide
Authors: Manusha Prathapaneni , Theyagarajan K. , Elancheziyan Mari , Shankar Harisingh , Thenmozhi Kathavarayan , Senthilkumar Sellappan
Published: July 2022

A simple and selective enzyme-free electrochemical sensor for H2O2 has been designed and fabricated using ionic liquid (IL) tagged anthraquinone (AQ) modified electrode (AQ-PF6-IL). This newly synthesized AQ-PF6-IL has been systematically characterized, after which it has been immobilized over a screen-printed electrode to produce AQ-PF6-IL/SPE. The electrochemical investigation of AQ-PF6-IL/SPE displayed a set of distinct redox peaks attributable to the anthraquinone/anthrahydroquinone redox pair. Interestingly, AQ-PF6-IL/SPE has shown enhanced peak current at reduced formal potential for AQ, when compared to AQ/SPE. Further, the electrocatalytic activity of AQ-PF6-IL/SPE towards the reduction of H2O2 was investigated with the sequential addition of H2O2. A rapid and appreciable enhancement in cathodic peak currents was observed and thus demonstrating the excellent electrochemical reduction of H2O2 at the newly developed sensor. Besides, AQ-PF6-IL/SPE established a good linear behaviour over a concentration range of 10–1228 μM with a high sensitivity of 0.281 μA μM−1 cm−2 and low detection limit of 2.87 μM. The fabricated sensor displayed excellent stability, good anti-interference ability, along with acceptable reproducibility. The superior properties of the developed sensor could be attributed to the newly designed AQ-PF6-IL, wherein the redox characteristics of AQ mediator are integrated with the high stability and conductivity of IL.
Bifurcation Analysis of a 5D Nutrient, Plankton, Limnothrissa miodon Model with Hydrocynus vittatus Predation
Authors: Mutasa Farikayi K. , Jones Brian , Tendaupenyu Itai H. , Nhiwatiwa Tamuka , Ndebele-Murisa Mzime R.
Published: July 2022

In this paper, we construct and analyze a theoretical, deterministic

5

D mathematical model of Limnothrissa miodon with nutrients, phytoplankton, zooplankton, and Hydrocynus vittatus predation. Local stability analysis results agree with the numerical simulations in that the coexistence equilibrium is locally stable provided that certain conditions are satisfied. The coexistence equilibrium is globally stable if certain conditions are met. Existence, stability, and direction of Hopf bifurcations are derived for some parameters. Bifurcation analysis shows that the model undergoes Hopf bifurcation at the coexistence point for the zooplankton growth rate with periodic doubling leading to chaos.
Multiclass Classification of Imagined Speech Vowels and Words of Electroencephalography Signals Using Deep Learning
Authors: Mahapatra Nrushingh Charan , Bhuyan Prachet
Published: July 2022

The paper’s emphasis is on the imagined speech decoding of electroencephalography (EEG) neural signals of individuals in accordance with the expansion of the brain-computer interface to encompass individuals with speech problems encountering communication challenges. Decoding an individual’s imagined speech from nonstationary and nonlinear EEG neural signals is a complex task. Related research work in the field of imagined speech has revealed that imagined speech decoding performance and accuracy require attention to further improve. The evolution of deep learning technology increases the likelihood of decoding imagined speech from EEG signals with enhanced performance. We proposed a novel supervised deep learning model that combined the temporal convolutional networks and the convolutional neural networks with the intent of retrieving information from the EEG signals. The experiment was carried out using an open-access dataset of fifteen subjects’ imagined speech multichannel signals of vowels and words. The raw multichannel EEG signals of multiple subjects were processed using discrete wavelet transformation technique. The model was trained and evaluated using the preprocessed signals, and the model hyperparameters were adjusted to achieve higher accuracy in the classification of imagined speech. The experiment results demonstrated that the multiclass imagined speech classification of the proposed model exhibited a higher overall accuracy of 0.9649 and a classification error rate of 0.0350. The results of the study indicate that individuals with speech difficulties might well be able to leverage a noninvasive EEG-based imagined speech brain-computer interface system as one of the long-term alternative artificial verbal communication mediums.
Effect of Self‐Determination Theory on Knowledge, Treatment Adherence, and Self‐Management of Patients with Maintenance Hemodialysis
Authors: Wu Rui , Feng Sifang , Quan Hongli , Zhang Yun , Fu Rong , Li Hong
Published: July 2022

Our aim of this study was to investigate the effect of self‐determination theory on awareness of relevant knowledge, treatment compliance, and self‐management level in maintenance hemodialysis (MHD) patients. A total of 90 patients who received MHD treatment in our hospital from April 2018 to June 2020 were selected and randomly divided into the intervention group and the routine group with 45 cases in each group. Both groups were given basic hemodialysis patient management measures, and the intervention group was given self‐determination theory to manage patients on this basis. The awareness rate of hemodialysis patient‐related knowledge, patient treatment compliance, patient self‐management scores, and quality of life scores were compared between the two groups before intervention and 6 months after intervention. After intervention, the awareness rate of dialysis principle, reasonable diet, internal fistula protection, and complication prevention knowledge in the intervention group was higher t (P < 0.05); before intervention, there was no significant difference in the scores of all dimensions of compliance between the two groups (P > 0.05); after intervention, the dietary compliance, fluid intake compliance, dialysis regimen compliance, and total score of compliance in the intervention group were higher than those in the conventional group (P < 0.05). After intervention, the total scores of problem solving, partnership, emotional processing, self‐care activities, and self‐management in the intervention group were higher than those in the conventional group, (P < 0.05). After intervention, the physical domain, psychological domain, social relationship domain, and total scores of quality of life in the intervention group were higher than those in the conventional group (P < 0.05). Self‐determination theory management is effective in improving the awareness of hemodialysis‐related knowledge, treatment compliance, self‐management level, and quality of life in MHD patients.
Image Space Accelerating Algorithm for Solving a Class of Multiplicative Programming Problems
Authors: Zhou Haoyu , Li Guohou , Gao Xueliang , Hou Zhisong
Published: July 2022

This paper interprets an image space accelerating branch and bound algorithm for globally solving a class of multiplicative programming problems (MP). In this algorithm, in order to obtain the global optimal solution, the problem (MP) is transformed into an equivalent problem (P2) by introducing new variables. By utilizing new linearizing relaxation technique, the problem (P2) can be converted into a series of linear relaxation programming problems, which provide the reliable lower bound in the branch and bound search. Meanwhile, an image space accelerating method is constructed to improve the computational performance of the algorithm by deleting the subintervals which have no global optimal solution. Furthermore, the global convergence of the algorithm is proved. The computational complexity of the algorithm is analyzed, and the maximum iterations of the algorithm are estimated. Finally, numerical experimental results show that the algorithm is robust and efficient.
Health Promotion System for the Elderly’s Daily Body Functions Based on Nanoprotective Technology
Authors: Hu Ruixia , Hao Qi
Published: July 2022

With the acceleration of China’s aging trend, the physical health of the elderly needs more attention and research from us. In particular, many elderly people are prone to accidental injuries during exercise, so how to protect the health of the elderly through high‐tech is worthy of more in‐depth research. This measure can promote the establishment of a health system for the elderly. This article is mainly based on the research on the health promotion system of the elderly’s daily body functions based on nanoprotective gear technology. It uses the literature method to read a lot of literature to understand the current research status of the elderly’s daily body function and health and the application status of nanotechnology, through the questionnaire and survey method for testing the blood pressure, heart rate, pulse wave velocity, bone density, and other physical indicators of the surveyed elderly population, and organize and analyze the relevant data. In this survey, 28.8% of the elderly who regularly participate in physical exercise have high systolic blood pressure, 33.3% of the elderly who do not participate in exercise regularly have high systolic blood pressure, and most of them are in the low bone density stage. Therefore, nanocare technology can effectively protect the health of the elderly and provide protection during exercise. The research in this article is aimed at illustrating the establishment and improvement of the physical function and health promotion system of the elderly through the impact data on physical indicators and providing a factual basis for the elderly to better participate in physical exercise and improve the health quality of the elderly.
Performance Simulation of Identification System Based on Improved Neural Network Algorithm
Authors: Zhao Zhaolong , Huang Minghui , Li Yibo
Published: July 2022

After decades of development, neural network theory has made considerable progress in many research fields such as pattern recognition, automatic control, signal processing, decision support, and artificial intelligence. This article discusses the application of neural networks in pattern recognition and system recognition and proposes several new methods for recognizing system models and recognizing model parameters. In order to achieve high-precision control of smart structure actuators, a robust model must first be created. For various modeling tasks, many scientists have done a good job and proposed different modeling methods. There are three main ways to create a system model: one is a physical model based on the mechanism of the material itself, the other is an operator model based on experimental phenomena, and the third is an intelligent model based on computer intelligence. The problem of the recognition system stems from the fact that with the development of science and technology, the research methods of various disciplines have been further quantified. In industrial practice and scientific experiments, the purpose of observing and calculating the quantitative identification of complex objects that need to be studied is usual. According to its inherent laws, it is necessary to establish a mathematical model of the research object in order to make decisions such as analysis, design, prediction, and control. This article uses the neural network model to study the best improvement method of the dynamic process. The research results of this article represent the theoretical basis of future scientific research to a certain extent and have important research value.
Automatic Recognition and Extraction of English Verb Types Based on Index Line Clustering
Authors: Zhao Hui , Jin Kexin , Wang Jing
Published: July 2022

Languages are not uniform and certain words are used differently by speakers of different languages more or less often, or with distinct meanings. In both linguistics and natural language processing (NLP) problems, the classification that groups together verbs and a collection of similar syntactic and semantic features are of great interest. In the modern era of science and technology, NLP technology is developing rapidly. However, the interpretation of index lines still needs to be realized manually. This method takes a long time, especially after entering the era of big data, the number of corpora has increased rapidly and it is normal to have a corpus with hundreds of millions of words. The quantity of text generated every day is increasing intensely and the word index based on search words is as high as tens of thousands of lines, so it is very difficult to analyze index lines manually. Automatic lexical knowledge acquisition is essential for a variety of NLP activities. Particularly knowledge about verbs is critical, which are the major source of relationship information in a sentence. Due to this issue, this study attempts to automatically identify and extract English verbs by index line clustering. Each index behavior can be regarded as microtext automatic clustering to realize the automatic identification and extraction of English verb forms. This study first focuses on the clustering index algorithm including the C-means clustering algorithm and fuzzy C-means clustering algorithm, then describes in detail the automatic recognition and extraction process of English verbs based on index line clustering, and creates a verification set and completes the index line clustering of English verbs. Finally, the effect of index line algorithm is analyzed from two aspects: automatic recognition of English verb types and recall rate. At the same time, the verbs are selected to analyze their types and judge the probability of each type. The experimental results show that the average recognition rate of English verbs in the manual classification is 91.01%, and the average accuracy of automatic recognition and extraction of English verb patterns based on index row clustering is 95.99%.
Model of Negative Emotional Information Communication among Netizens under Corporate Negative Events
Authors: Shi Rui , Hou Xiaoyong , Liu Chang
Published: July 2022

Corporate negative events have been frequently exposed in the information age. Research on the dissemination mechanism of negative emotional information for netizens contributes corporates to monitor public opinion trends and resolve public opinion crises. Combining emotional infection theory and the classic infectious disease model, we first divided emotional communication stage into individual and group propagation stages and constructed the SEI1I2R model of negative emotional information communication among netizens under corporate negative events. Then, we performed the model analysis and simulation. Results indicated that trust and communication conversion rates are in direct proportion to negative emotional diffusion speed; corporate response rate is inversely proportional to negative emotional diffusion speed; however, the effects of self-regulation are insignificant.
Application of Robust Data Link Optimization in Medical Protein Nutrition Intervention Based on Ensemble Learning Algorithms
Authors: Hou Xiaowen
Published: July 2022

Research on 6G-related technologies based on the Internet of Things (IoT) has attracted widespread attention from research units, universities, and industry, and there are still some important issues that need to be resolved urgently. How to ensure robust data link optimization for data transmission in a band-limited nonstationary environment at a lower cost is a very important issue. Based on the current research status of specific medical protein POCT products at home and abroad, this paper has designed and developed a specific medical protein detection system with simple operation, rapid detection, and small volume: according to the Lambert Beer’s law, we adopt the transmission colorimetry method (transmittance Turbidity method), with the optical part, electronic part, and mechanical part as the structure, carry out the design work of optoelectronics and software. It also evaluates the serum nutritional indicators and gastrointestinal function of abdominal surgery patients after nutritional intervention combined with emotional nursing and expands the reference basis for intervention methods for abdominal surgery patients. Using this experimental method, after the samples are selected by the CART algorithm, the accuracy of the running results is higher.
Plant Bioactives in the Treatment of Inflammation of Skeletal Muscles: A Molecular Perspective
Authors: Karati Dipanjan , Varghese Ryan , Mahadik K. R. , Sharma Rohit , Kumar Dileep
Published: July 2022

Skeletal muscle mass responds rapidly to growth stimuli, precipitating hypertrophies (increased protein synthesis) and hyperplasia (activation of the myogenic program). For ages, muscle degeneration has been attributed to changes in the intracellular myofiber pathways. These pathways are tightly regulated by hormones and lymphokines that ultimately pave the way to decreased anabolism and accelerated protein breakdown. Despite the lacunae in our understanding of specific pathways, growing bodies of evidence suggest that the changes in the myogenic/regenerative program are the major contributing factor in the development and progression of muscle wasting. In addition, inflammation plays a key role in the pathophysiology of diseases linked to the failure of skeletal muscles. Chronic inflammation with elevated levels of inflammatory mediators has been observed in a spectrum of diseases, such as inflammatory myopathies and chronic obstructive pulmonary disease (COPD). Although the pathophysiology of these diseases varies greatly, they all demonstrate sarcopenia and dysregulated skeletal muscle physiology as common symptoms. Medicinal plants harbor potential novel chemical moieties for a plenitude of illnesses, and inflammation is no exception. However, despite the vast number of potential antiinflammatory compounds found in plant extracts and isolated components, the research on medicinal plants is highly daunting. This review aims to explore the various phytoconstituents employed in the treatment of inflammatory responses in skeletal muscles, while providing an in-depth molecular insight into the latter.
Numerical Calculation of Three-Dimensional Ground State Potential Energy Function of Na2F System
Authors: Wang Yue , Liu Yu , Fang BiLv , Gao Gan , Zhang Chengwen , Dong Dezhi
Published: July 2022

We present a new three-dimensional global potential energy surface (PES) for the ground state of Na2F system. A total of about 1460 points were generated for the PES. All of the points have been carried out by using the coupled-cluster single-, double-, and perturbative triple-excitations [CCSD(T)]. Two Jacobi coordinates,

R

and

θ

, and the frozen molecular equilibrium geometries were used. We mixed the basis sets of aug-cc-pCVQZ for the sodium atom and the basis sets of aug-cc-pCVDZ for the fluorine atom with an additional (3s3p2d) set of midbond functions; the energies obtained were extrapolated to the complete basis set limit. The whole calculation adopted supramolecular approximation approach. We divided the potential energy surface into three regions, the peak region, the well region, and the long range region, and calculate the single point energy, respectively. Our ab initio calculations will be useful for future studies of the collision-induced absorption for the Na2-F dimer, and it can be used for modeling the dynamical behavior in Na2F system too.
Carbon Market Evaluation Based on Random Walk Hypothesis in China
Authors: Zhang Tian , Zou Shaohui
Published: July 2022

According to the proposal of Kyoto protocol, carbon dioxide emission rights are traded as a commodity, and carbon emission trading market emerges as the times require. As the world’s largest carbon emitter, China has established eight pilot markets for carbon emission trading. Selecting the closing price of eight carbon trading markets from the establishment to June 23, 2020, this paper analyzes the daily, weekly, and monthly return series data, using the first-order autoregressive process to adjust the daily income series to eliminate the weak trading market effect and then comprehensively uses the traditional variance ratio test and multiple variance ratio test to analyze the weak-form market efficiency of the eight carbon trading markets. The empirical results show that most of the carbon trading markets are non-weak-form market efficiency, and only Tianjin, Shanghai, and Hubei markets are weak-form market efficiency under the daily trading data. However, with the increase of carbon holding period, the weak-form market efficiency continues to strengthen. It shows that liquidity, quantity, and information transparency are important factors that affect the market efficiency.
Identification of Cardiac Patients Based on the Medical Conditions Using Machine Learning Models
Authors: Kumar Krishna , Kumar Narendra , Kumar Aman , Mohammed Mazin Abed , Al-Waisy Alaa S. , Jaber Mustafa Musa , Pandey Neeraj Kumar , Shah Rachna , Saini Gaurav , Eid Fatma , Al-Andoli Mohammed Nasser
Published: July 2022

Chronic diseases are the most severe health concern today, and heart disease is one of them. Coronary artery disease (CAD) affects blood flow to the heart, and it is the most common type of heart disease which causes a heart attack. High blood pressure, high cholesterol, and smoking significantly increase the risk of heart disease. To estimate the risk of heart disease is a complex process because it depends on various input parameters. The linear and analytical models failed due to their assumptions and limited dataset. The existing studies have used medical data for classification purposes, which help to identify the exact condition of the patient, but no one has developed any correlation equation which can be directly used to identify the patients. In this paper, mathematical models have been developed using the medical database of patients suffering from heart disease. Curve fitting and artificial neural network (ANN) have been applied to model the condition of patients to find out whether the patient is suffering from heart disease or not. The developed curve fitting model can identify the cardiac patient with accuracy, having a coefficient of determination (R2-value) of 0.6337 and mean absolute error (MAE) of 0.293 at a root mean square error (RMSE) of 0.3688, and the ANN-based model can identify the cardiac patient with accuracy having a coefficient of determination (R2-value) of 0.8491 and MAE of 0.20 at RMSE of 0.267, it has been found that ANN provides superior mathematical modeling than curve fitting method in identifying the heart disease patients. Medical professionals can utilize this model to identify heart patients without any angiography or computed tomography angiography test.
Design and Clinical Application Effect Analysis of a New Type of Oral Fluid Suction Device for Patients with Orotracheal Intubation
Authors: Qian Yanying , Lu Haifei
Published: July 2022

Tracheal intubation is an important access to general anesthesia surgery or respiratory support in critically ill patients. Orotracheal intubation is the most common method of establishing artificial airways in clinical practice. Tracheal intubation and mechanically assisted breathing are among the important steps in the clinical rescue of critically ill patients. During tracheal intubation, it easily causes iatrogenic skin damage, unclear mouth, ulcer, and other oral mucosal complications due to the improper fixation method and excellent dental pads. Therefore, the purpose of this study was to design a novel oral suction device for patients with orotracheal intubation and explore its safety, convenience, and comfort in the clinical application of orotracheal intubation patients. From October 2016 to April 2017, a total of 232 patients with mechanical ventilation through orotracheal intubation in the Department of Surgery and Critical Care Medicine were selected by the convenience sampling method. According to the random number table method, 232 cases were divided into the experimental group and control group, with 116 cases in each group. The experimental group used a self‐designed oral fluid suction device to fix the tracheal intubation; the control group used the traditional method, placing ordinary disposable tooth pads, and then using 3M tape to fix the tracheal intubation. The incidence of oral mucosa and lip pressure ulcers, patient comfort, and tracheal tube displacement were observed and compared between the two groups. The incidence of oral mucosa and lip pressure ulcers in the observation group using the self‐designed oral fluid suction device to fix the tracheal intubation was significantly lower than that in the control group. At the same time, the comfort of the patients was significantly higher than that of the control group, the incidence of tracheal tube displacement was significantly lower than that of the control group, and the differences between the above indicators were statistically significant (P < 0.05). For patients with orotracheal intubation, using a new oral fluid suction device to fix the tracheal intubation can effectively prevent the displacement of the tracheal intubation, protect the oral mucosa and lips, and reduce the pain of the patient. At the same time, the oral fluid suction device designed in this study has low production cost, strong practicability, and is suitable for clinical promotion.
Prevalence and Risk Factors of Vulvovaginal Candidosis during Pregnancy: A Review
Authors: Disha Tasfia , Haque Fahim
Published: July 2022

Vulvovaginal candidosis (VVC) is a symptomatic vaginal yeast infection, especially caused by Candida spp. Although VVC is common among reproductive-age women, prevalence studies notice the uprise of vaginal Candida colonization to 30% during pregnancy by culture, especially in the last trimester. Recent studies have considered it a severe problem due to the emerging evidence showing the association of VVC with a higher chance of pregnancy-related complexities (e.g., preterm labor, premature rupture of membranes, congenital cutaneous candidosis, and chorioamnionitis). In this review, we have reassessed and summarized the prevalence rate of VVC in expecting mothers and analyzed the association of several factors to the increased risk of VVC during pregnancy in different regions of the world. Altogether, these data collected from various studies showed the highest prevalence of VVC during pregnancy, mostly in Asian and African countries (90.38%, 62.2%, and 61.5% in Kenya, Nigeria, and Yemen, respectively). The prevalence rate of VVC during pregnancy was also found to differ with age, gestation period, parity, educational status, and socioeconomic level. Some pregnancy-related factors (e.g., weakened immunity; elevated level of sex hormones, glycogen deposition; low vaginal pH; decreased cell-mediated immunity) and several clinical and behavioral factors can be suggested as potential risk factors of candidosis during pregnancy.
Design of Mechanical Properties of Poly(butylene-adipate-terephthalate) Reinforced with Zein-TiO2 Complex
Authors: Togliatti Elena , Grimaldi Maria , Pitirollo Olimpia , Cavazza Antonella , Pugliese Diego , Milanese Daniel , Sciancalepore Corrado
Published: July 2022

Mechanical properties of polymer biocomposites are influenced by the interaction between the matrix and the filler surface. In this work, composites based on poly(butylene-adipate-terephthalate) (PBAT) filled with micrometric particles of zein-TiO2 complex (ZTC) were realized via solvent casting technique at different concentrations, equal to 0, 5, 10, and 20 wt%. After pelletization, the resulting materials were injection molded into standard specimens, employed for the uniaxial tensile test (UTT) characterization. From the stress-strain curves, Young’s modulus (

E

), yield stress (

σ

y

), stress at break (

σ

B

), elongation at break (

ε

B

), and toughness (

T

) were collected. The addition of the ZTC proved to show a reinforcing effect on the polymeric matrix, with an increase in both

E

and

σ

y

. Modelling of the mechanical properties was performed by applying Kerner’s and Pukánszky’s equations. Kerner’s model, applied on experimental

E

values, returned a very good correspondence between collected and theoretical values. From the application of Pukánszky’s model to

σ

y

, the obtained

B

value showed a good interfacial interaction between the matrix and the filler. Due to the enhanced stiffness of the composites, a reduction in the true stress at break (

σ

T
,
B

) was observed. The modified Pukánszky’s model gave a

B

value lower than the one obtained for the yield, but still in the range of acceptable values for microcomposites.