bluuebunny/crossref_metadata_2025 huggingface dataset:
Buckling Analysis of Cylindrical Shells with Variable Thickness Subjected to Non-Uniform Axial Compression by Establishing a Novel Quadratic Perturbation Technique
Authors: Yang Licai , Qiu Tian , Dong Yuanyuan
Published: April 2022

This paper analytically studies the buckling of a cylindrical shell having varying thickness under non-uniform axial compressive loads for the first time, which widely exists in engineering practice. A novel quadratic perturbation technique is developed to establish general buckling load formulas for the shell. This method overcomes the difficulties of traditional energy methods in solving high order determinants and deriving direct expressions for buckling loads when shell thickness and axial load are unknown. Applying presented formulas, various shell thicknesses and axial loads are analyzed, and a series of new results for buckling loads are obtained and validated. Even for classical cosine thickness variation under uniform axial compression, we also give general conclusions compared with Koiter’s results by the energy method. The effects of thickness variations and load distribution parameters on buckling loads are analyzed in detail. The presented study in this paper fills the gap and establishes a foundation of buckling analysis for non-uniformly loading cylindrical shells with variable thickness. Certainly, the established formulas are general and available for buckling resistance capacity evaluation for the shells under all circumstances involving thickness variations or/and non-uniform axial compressive loads.
Dynamical System Analysis of a Mathematical Model of Mild Atherosclerosis
Authors: Mukherjee Debasmita
Published: April 2022

Atherosclerosis, a long-term inflammatory affliction, occurs due to plaque accumulation in the intima, the innermost layer of artery. It is one of the prime causes of deaths from several cardio-vascular diseases. The primary interaction between oxidized low density lipoprotein (LDL) and macrophages in forming fatty steak is presented here in terms of a nonlinear reaction-diffusion system in one spatial dimension under Neumann boundary conditions. Both the spatio-temporal and the temporal models are analyzed qualitatively. Numerical simulations of both the model systems lead to three significant model parameters with respect to which bifurcations are found. Bifurcating parameters indicate possible developments of various therapeutic strategies in order to establish any preventative measures for an individual or group of patients. It has been found that ingestion rate of oxidized LDL by macrophages has a great impact on the plaque forming process.
Subspace-based nonzero component graph of a finite dimensional vector space
Authors: Pirzada S. , Wani Bilal A. , Al-Kaseasbeh Saba
Published: April 2022

Let [Formula: see text] be a vector space over a field [Formula: see text] with basis [Formula: see text]. Let [Formula: see text] be an [Formula: see text]-dimensional subspace of [Formula: see text] with basis [Formula: see text], where [Formula: see text] is some [Formula: see text]. We introduce the subspace-based nonzero component graph, denoted by [Formula: see text], of the finite dimensional vector space [Formula: see text] with respect to [Formula: see text] and [Formula: see text] as follows: [Formula: see text] and for [Formula: see text], there is an edge between [Formula: see text] and [Formula: see text] if and only if either [Formula: see text] or [Formula: see text], where [Formula: see text], [Formula: see text] and [Formula: see text]. We investigate the connectivity, diameter and completeness of [Formula: see text]. Further, we find its domination number and independence number. If [Formula: see text] is a subspace of a vector space, we show that the graph is complete if and only if [Formula: see text] is a hyperspace. Finally, we determine the degree of each vertex in case the base field is finite.
Characterization and behaviour of soils: actual objectives and requirements
Authors: Voznesensky Eugene A.
Published: April 2022

The paper discusses several important and interesting issues of modern studies of soil behaviour. After a series of milestones in the history of human geotechnical achievements and development of classical soil mechanics, humanity nowadays is facing several challenges where other – mostly numerical – solutions are required. They are provided by various soil models. Some modern models are based on specific soil parameters, often not known in classical soil mechanics. Scientific understanding of their nature as well as new techniques and high-precision testing equipment for their experimental determination are necessary for progress in such research. Even water compressibility influences the accuracy of pore pressure measurements in advanced soil testing. The ‘front line’ of modern studies in soil behaviour is in non-linear soil mechanics. Detailed consideration of the nature of this complex non-linearity, especially in the small-strain range, reveals its close relationship to microstructural and energy transformations of soils, and the development of an instability state as a preparation for failure. Finally, processes causing the observed effects are presented in terms of their relationship with important threshold strain levels.

Thematic collection:
This article is part of the Leading to Innovative Engineering Geology Practices collection available at:
https://www.lyellcollection.org/cc/Leading-to-Innovative-Engineering-Geology-Practices
3D Reconstruction of Moroccan-Andalusian Muqarnas Domes
Authors: Senhaji Mohammed , Benslimane Rachid
Published: April 2022

Muqarnas is a three-dimensional Islamic architecture decoration conceived as a spatial arrangement of 3D shapes according to precise geometric rules. Muqarnas work can be performed on several types of materials such as stone, wood, plaster or brick. This paper aims to propose an algorithmic method to construct the circular Moroccan-Andalusian Muqarnas Ornaments from their 2D representation models. The principle is to extract from a 2D design geometric information and underlying rules useful for the construction of its corresponding 3D model. To do so, the first stage consists to determine for each 2D shape of the 2D design model its corresponding 3D Muqarnas element. The second stage consists to perform the spatial composition of these identified 3D elements by using their neighborhood properties. The performance of the proposed method is evaluated by its capacity to reconstruct historical circular Muqarnas ornaments appeared during the Almoravids (1040-1147) and Almohads periods (1147-1269). This performance is further evaluated by the ability of the proposed method to construct all possible variants of a historical 3D Muqarnas ornament by considering the different 3D shapes which can be associated to the same 2D shape.
Cost Sharing over Combinatorial Domains
Authors: Birmpas Georgios , Markakis Evangelos , Schäfer Guido
Published: April 2022

We study the problem of designing cost-sharing mechanisms for combinatorial domains. Suppose that multiple items or services are available to be shared among a set of interested agents. The outcome of a mechanism in this setting consists of an assignment, determining for each item the set of players who are granted service, together with respective payments. Although there are several works studying specialized versions of such problems, there has been almost no progress for general combinatorial cost-sharing domains until recently [
9
]. Still, many questions about the interplay between strategyproofness, cost recovery, and economic efficiency remain unanswered.

The main goal of our work is to further understand this interplay in terms of budget balance and social cost approximation. Towards this, we provide a refinement of cross-monotonicity (which we term
trace-monotonicity
) that is applicable to iterative mechanisms. The trace here refers to the order in which players become finalized. On top of this, we also provide two parameterizations (complementary to a certain extent) of cost functions, which capture the behavior of their average cost-shares.

Based on our trace-monotonicity property, we design an Iterative Ascending Cost-Sharing Mechanism, which is applicable to the combinatorial cost-sharing setting with symmetric submodular valuations. Using our first cost function parameterization, we identify conditions under which our mechanism is weakly group-strategyproof,

\( O(1) \)

-budget-balanced, and

\( O(H_n) \)

-approximate with respect to the social cost. Furthermore, we show that our mechanism is budget-balanced and

\( H_n \)

-approximate if both the valuations and the cost functions are symmetric submodular; given existing impossibility results, this is best possible.

Finally, we consider general valuation functions and exploit our second parameterization to derive a more fine-grained analysis of the Sequential Mechanism introduced by Moulin. This mechanism is budget balanced by construction, but in general, only guarantees a poor social cost approximation of

\( n \)

. We identify conditions under which the mechanism achieves improved social cost approximation guarantees. In particular, we derive improved mechanisms for fundamental cost-sharing problems, including Vertex Cover and Set Cover.
Spirals in Galaxies
Authors: Sellwood J.A. , Masters Karen L.
Published: April 2022

Spirals in galaxies have long been thought to be caused by gravitational instability in the stellar component of the disk, but discerning the precise mechanism had proved elusive. Tidal interactions, and perhaps bars, may provoke some spiral responses, but spirals in many galaxies must be self-excited. We survey the relevant observational data and aspects of disk dynamical theory. The origin of the recurring spiral patterns in simulations of isolated disk galaxies has recently become clear, and it is likely that the mechanism is the same in real galaxies, although evidence to confirm this supposition is hard to obtain. As transient spiral activity increases random motion, the patterns must fade over time unless the disk also contains a dissipative gas component. Continuing spiral activity alters the structure of the disks in other ways: reducing metallicity gradients and flattening rotation curves are two of the most significant. The overwhelming majority of spirals in galaxies have two- or three-fold rotational symmetry, indicating that the cool, thin disk component is massive. Spirals in simulations of halo-dominated disks instead manifest many arms and, consequently, do not capture the expected full spiral-driven evolution. We conclude by identifying areas where further work is needed.
Galaxy Formation and Reionization: Key Unknowns and Expected Breakthroughs by the James Webb Space Telescope
Authors: Robertson Brant E.
Published: April 2022

The launch of the James Webb Space Telescope (JWST) in late 2021 marks a new start for studies of galaxy formation at high redshift ( z ≳ 6) during the era of cosmic reionization. JWST can capture sensitive, high-resolution images and multiobject spectroscopy in the IR that will transform our view of galaxy formation during the first billion years of cosmic history. This review summarizes our current knowledge of the role of galaxies in reionizing intergalactic hydrogen ahead of JWST, achieved through observations with the Hubble Space Telescope and ground-based facilities including Keck, the Very Large Telescope, Subaru, and the Atacama Large Millimeter/Submillimeter Array. We identify outstanding questions in the field that JWST can address during its mission lifetime, including with the planned JWST Cycle 1 programs. These findings include the following: ▪ Surveys with JWST have sufficient sensitivity and area to complete the census of galaxy formation at the current redshift frontier ( z ∼ 8–10). ▪ Rest-frame optical spectroscopy with JWST of galaxies will newly enable measures of star-formation rate, metallicity, and ionization at z ∼ 8–9, allowing for the astrophysics of early galaxies to be constrained. ▪ The presence of evolved stellar populations at z ∼ 8–10 can be definitively tested by JWST, which would provide evidence of star formation out to z ∼ 15.
Risks and Global Supply Chains: What We Know and What We Need to Know
Authors: Baldwin Richard , Freeman Rebecca
Published: April 2022

Recent supply disruptions catapulted the issue of risk in global supply chains (GSCs) to the top of policy agendas and created the impression that shortages would have been less severe if GSCs had been either shorter and more domestic or more diversified. But is this right? We start our answer by reviewing studies that look at risks to and from GSCs and at how GSCs have recovered from past shocks. We then look at whether GSCs are too risky, starting with business research on how firms approach the cost-resilience trade-off. We propose the risk-versus-reward framework from portfolio theory as a good way to evaluate whether anti-risk policy is justified. We then discuss how exposures to foreign shocks are measured and argue that exposure is higher than direct indicators imply. Finally, we consider the future of GSCs in light of current policy proposals and advancing technology before pointing to the rich menu of topics for future research on the risk-GSC nexus.
Diverse Molecular Mechanisms Underlying Pathogenic Protein Mutations: Beyond the Loss-of-Function Paradigm
Authors: Backwell Lisa , Marsh Joseph A.
Published: April 2022

Most known disease-causing mutations occur in protein-coding regions of DNA. While some of these involve a loss of protein function (e.g., through premature stop codons or missense changes that destabilize protein folding), many act via alternative molecular mechanisms and have dominant-negative or gain-of-function effects. In nearly all cases, these non-loss-of-function mutations can be understood by considering interactions of the wild-type and mutant protein with other molecules, such as proteins, nucleic acids, or small ligands and substrates. Here, we review the diverse molecular mechanisms by which pathogenic mutations can have non-loss-of-function effects, including by disrupting interactions, increasing binding affinity, changing binding specificity, causing assembly-mediated dominant-negative and dominant-positive effects, creating novel interactions, and promoting aggregation and phase separation. We believe that increased awareness of these diverse molecular disease mechanisms will lead to improved diagnosis (and ultimately treatment) of human genetic disorders.
Design and Characterization of Host Frameworks for Facile Magnesium Transport
Authors: Gao Yirong , Mishra Tara P. , Bo Shou-Hang , Sai Gautam Gopalakrishnan , Canepa Pieremanuele
Published: April 2022

The development of inexpensive batteries based on magnesium (Mg) chemistry will contribute remarkably toward developing high-energy-density storage systems that can be used worldwide. Significant challenges remain in developing practical Mg batteries, the chief of which is designing materials that can provide facile transport of Mg. In this review, we cover the experimental and theoretical methods that can be used to quantify Mg mobility in a variety of host frameworks, the specific transport quantities that each technique is designed to measure or calculate, and some practical examples of their applications. We then list the unique challenges faced by different experimental and computational techniques in probing Mg ion transport in materials. This review concludes with an outlook on the directions that the scientific community could soon pursue as we strive to construct a pragmatic Mg battery.
Considering Organismal Physiology in Laboratory Studies of Rodent Behavior
Authors: Rubio Arzola Patricia , Shansky Rebecca M.
Published: April 2022

Any experiment conducted in a rodent laboratory is done so against the backdrop of each animal's physiological state at the time of the experiment. This physiological state can be the product of multiple factors, both internal (e.g., animal sex, strain, hormone cycles, or circadian rhythms) and external (e.g., housing conditions, social status, and light/dark phases). Each of these factors has the potential to influence experimental outcomes, either independently or via interactions with others, and yet there is little consistency across laboratories in terms of the weight with which they are considered in experimental design. Such discrepancies—both in practice and in reporting—likely contribute to the perception of a reproducibility crisis in the field of behavioral neuroscience. In this review, we discuss how several of these sources of variability can impact outcomes within the realm of common learning and memory paradigms.
Recent Advances in Energy Field Assisted Hybrid Electrodeposition and Electroforming Processes
Authors: Biswal Hrudaya , Gupta Ankur
Published: April 2022

Being an additive manufacturing process with a bottom-up approach, electrodeposition principle has opened up new avenues for the multiscale metallic deposition and because of its attainment of high precision and dimensional accuracy, it has equally received the attention of researchers as well as of various industrial sectors. Considering the utility of this inexpensive manufacturing process, the present review paper deals firstly with the fundamental principles and applications of both electrodeposition and electroforming processes along with the as?sociated challenges and shortcomings. Thereafter, various energy ?eld-assisted methods such as abrasive-assisted, ultrasonic-assisted, magnetic-assisted, local?ized, photo-assisted, and gravity-assisted electrodeposition/electroforming have been discussed. Research progress associated with these techniques has been elaborately explained along with their principle of operation. The fabrication of various functional coatings, nanostructures, nanocomposites using such hybrid techniques have also been explored. In short, this review article throws light on the immense manufacturing possibilities in the area of electrodeposi?tion/electroforming that can be taken up as future scope in the areas of high?performance coatings, micro/nano-manufacturing and functional materials, etc.
Augmented Reality-Centered Position Navigation for Wearable Devices with Machine Learning Techniques
Authors: Kamalam G. K. , Joshi Shubham , Maheshwari Manish , Selvan K. Senthamil , Jamal Sajjad Shaukat , Vairaprakash S. , Alhassan Musah
Published: April 2022

People have always relied on some form of instrument to assist them to get to their destination, from hand-drawn maps and compasses to technology-based navigation systems. Many individuals these days have a smartphone with them at all times, making it a common part of their routine. Using GPS technology, these cellphones offer applications such as Google Maps that let people find their way around the outside world. Indoor navigation, on the other hand, does not offer the same level of precision. The development of indoor navigation systems is continuously ongoing. Bluetooth, Wi-Fi, RFID, and computer vision are some of the existing technologies used for interior navigation in current systems. In this article, we discuss the shortcomings of current indoor navigation solutions and offer an alternative approach based on augmented reality and ARCore. Navigating an indoor environment is made easier with ARCore, which brings augmented reality to your smartphone or tablet.
Research on Scheduling Algorithm of Agricultural Machinery Cooperative Operation Based on Particle Swarm Neural Network
Authors: Li Wei
Published: April 2022

In order to improve the cooperative operation scheduling effect of agricultural machinery, this article uses particle swarm neural network to study the cooperative operation scheduling algorithm of agricultural machinery and improves the cooperative scheduling effect of intelligent agricultural machinery. Aiming at the mixed integer nonlinear programming problem, this article proposes a collaborative algorithm of population intelligence and linear programming. The outer layer of the algorithm uses the improved particle swarm algorithm IPSO module, the inner layer uses the simplex algorithm SIM module, and the optimal solution of the MINLP problem is obtained through the iterative update of the inner and outer modules. The experimental study shows that the cooperative operation scheduling model of agricultural machinery based on particle swarm neural network proposed in this article can play an important role in modern agricultural planting and effectively improve the efficiency of agricultural planting.
Incremental Adaptive Control of a Class of Nonlinear Nonaffine Systems
Authors: Zhan Yizhao , Zou Shengxiang , He Xiongxiong , Sun Mingxuan
Published: April 2022

As a class of familiar nonlinear systems, nonaffine systems are frequently encountered in practical applications. Currently, in the context of learning control, there is a lack of research results about such general class of nonlinear systems, especially for the case of performing infinite interval tasks. This article focuses on the incremental adaptive control for nonlinear systems in nonaffine form, without requiring periodicity or repeatability. Instead of using the integral adaptation, incremental adaptive mechanisms are developed and the corresponding control schemes are presented, by which the numerical integration for implementation can be avoided. With the proposed incremental adaptation, the implicit function theorem is introduced to solve the intractability problem of the nonaffine structure. The robustness (robust convergence) of the tracking error is characterized, with the aid of a proposed key lemma, while the boundedness of all the variables is examined. Numerical results are presented to verify the effectiveness of the proposed control design.
Polish Experiences of Pain Treatment by Paramedics in relation to Good Practices of Pain Treatment: A Register-Based Study
Authors: Koral Mariusz , Szyller Jakub
Published: April 2022

Background. Pain is frequently encountered in the prehospital setting. The treatment of pain is one of the priority activities for paramedics. Methods. 1048576 patients under the care of EMS over a 7-month period were included in this study. Data included using pain scale and pain score, the initial diagnosis, analgesic treatment and the given drug obtained from the Ambulance Emergency Procedure Cards. Results. The complete absence of pain was detected in 43.9% (95% CI 43.8–44.0) of patients, mild pain in 17.0% (95% CI 16.9–17.1), moderate pain in 11.6% (95% CI 11.5–11.7), and severe pain in 4.9% (95% CI 4.9–4.9) of cases. In 22.5% (95% CI 22.4–22.6), no pain rating scale was used. 98.0% (95% CI 97.9–98.1) of patients with mild, 96.1% (95% CI 96.0–96.2) with moderate, and 94.0% (95% CI 93.9–94.1) with severe pain did not receive any pain medication. The most commonly used drugs in all groups were metamizole and ketoprofen. The greatest analgesic effect was observed in the group of patients with severe pain. Pain decreased by an average of 3.2 NRS points in the drug-free group and 3.1 NRS points in the treated group. The most commonly documented diagnoses in all groups of patients were signs and diseases not classified in other groups (ICD-10 R00-R99), injuries and consequences of external causes (ICD-10 S00-T98), and diseases of the circulatory system (ICD-10 I00–I99). Conclusions. Paramedics may use a variety of medications but use them rarely. Adding metamizole to the list of medications used by paramedics has made it one of the most commonly used drugs. Regardless of whether the drug was used or not, the analgesic effect was similar.
Dynamic Property Analysis of Orthotropic Bridge Deck with Local Fatigue Crack
Authors: Li Zhao , Zhang Jiarui , Zhu Yaoyang , Tu Jianwei
Published: April 2022

With the increase in traffic volume and overweight vehicles, fatigue problems are highlighted. Especially for the orthotropic steel deck, the premature cracks seriously affect the service performance, operation, and service quality of the bridge. In this paper, the steel box‐girder segment model and deck substructure model considering several typical fatigue cracks were established to reveal the variation law of fatigue cracks on vibration frequency and deflection under vehicle traveling load. A natural frequency analysis was performed to evaluate the change in the vibration characteristics due to localized damage, together with a dynamic analysis that considered a vehicle traveling on the bridge. The results show that the initiation and propagation of several typical cracks have little influence on the lower modes of the overall structure and great influence on the high‐order modes of the substructure. The dynamic deflection response of the bridge changed under vehicle load as damage progressed. Therefore, it was possible to identify cracks of the orthotropic steel bridge deck and to provide guidance for fatigue crack detection and repair.
The Construction of Online Course Learning Model of Ideological and Political Education for College Students from the Perspective of Machine Learning
Authors: Wu Huanhuan , Ma Lingang
Published: April 2022

Student behaviour analysis in the classroom is an important part of teaching and educational innovations that can assist the institution find an effective strategy to improve students’ learning efficiency and ability to innovate. In this study, a human behavior recognition system is proposed for monitoring the learning status of students in the course of ideological and political education using the signals of smartphone embedded gravity sensors. A convolution neural network (CNN) is used to automatically extract prominent patterns from the raw signals of smartphone embedded sensors followed by the classification of the seven student activities including walking, going upstairs, downstairs, lying, sitting, standing, and running, respectively. The optimized CNN model was obtained after training on 1,500 training samples of student’s behavior data. The model is evaluated in terms of evaluation metrics such as accuracy, precision, and recall. The proposed model achieved 97.83% accuracy, 97.82% precision, and 97.83% recall, respectively, which are significantly higher than the classification performance of the other recognition models. The proposed model achieved inspiring performance compared to the existing behavior recognition systems. The model of human behavior can obtain the learning state behavior of the students from the college students’ listening equipment, to understand the learning situation of the students.
Evaluation of “Online and Offline” Integrated Teaching Model of Ideological and Political Courses in Colleges and Universities in the Era of Artificial Intelligence
Authors: Wang Lijun
Published: April 2022

In recent years, the online mode of teaching and learning has been developing day by day. Educational organisations strive to achieve the goal of providing students with a good education. This curiosity resulted in the development of many tools and techniques. The teachers and students should also be trained to adapt to the new technology-based classes from the traditional model. In this research, an integrated model is designed to implement an artificial neural network (ANN) in the intelligent wireless neural network to provide timely delivery of the courses. The integrated model is the combined online and offline teaching and learning process framework. The system is trained with ANN to increase the teaching and learning performance, and the model is compared from various perspectives with existing models like

K

-means and hybrid

K

-means. The comparison is carried on for two student batches, and the results show that the proposed ANN model provides an accuracy of 96% and 98% for batch one and batch two of students, respectively.
A Novel Psychotherapy Effect Detector of Public Art Based on ResNet and EEG Imaging
Authors: Tian Tingyi , Wang Le , Luo Man , Zhu Wei
Published: April 2022

Background and Objective. In view of the imperfection of the current public art evaluation system and practical utility detection system, this paper designs and invents a psychotherapy effect detector of public art based on ResNet network and EEG imaging. The detector includes sensor signal acquisition unit, signal storage and transmission unit, and human-computer interaction visualization unit. The detector is designed in order to test the psychotherapy effect of public art and quantify the public’s evaluation of public art to improve the public art evaluation system. Methods. The EEG emotion dataset seed is used for feature extraction with DE, and the emotion is recognized by ResNet. Adam optimizer is used to classify the extracted DE through ResNet50 model. Each batch is set as 5 groups of data and is trained for 50 rounds, then the model is optimized, and the accuracy rate is 76.47%, which output the probability of good emotion through the model. We put the model optimized by ResNet into the intelligent module and visualize it with numerical value. Results. The detector designed by EEG data and ResNet50 optimization model has high accuracy. The results show that the error between the detector data and the questionnaire interview data is small, the average error is 2.77, and the accuracy is 97%. The closer the subject’s emotion before the test is to neutral emotion, the closer the questionnaire result is to the test result of the tester, and the smaller the error is. The difference between the tester data and the survey questionnaire data is 0.2, which is in good agreement and has small error. It can be seen that the detector has high accuracy. Conclusion. Our proposed public art psychotherapy effect detector has good accuracy in detecting users’ emotions. It can detect the group psychotherapy effect of public art and can classify and screen a large number of public arts in the city by quantitative methods. It provides support for further summarizing the practical utility of public art and provides a new way for the optimal design and follow-up evaluation of public art design.
Integrated Network Pharmacology and Gut Microbiota Study on the Mechanism of Huangqin Decoction in Treatment Diabetic Enteritis
Authors: Xu Xiaomin , Fang Cheng , Lu Fang , Liu Shumin
Published: April 2022

Objective. Using network pharmacology and gut microbiota sequencing to investigate the probable mechanism of Huangqin decoction in the treatment of Diabetic enteritis (DE). Methods. The mechanism of Huangqin decoction on DE was studied by combining network pharmacology and gut microbiota sequencing analysis. The core components and possible targets of Huangqin decoction were analyzed by network pharmacology. The effect of Huangqin decoction on microorganisms was investigated by gut microbiota sequencing. Results. The results of gut microbiota sequencing analysis showed the abundance of TM7, Tenericutes, Chloroflexi, Cyanobacteria, Acidobacteria, WS6, [Prevotella], Helicobacter, Prevotella, Lactococcus, and Anaeroplasma in the Huangqin decoction group had a significant downward. Using a network pharmacology-related database, 141 main active components of Huangqin decoction were identified, as well as 256 corresponding component targets and 1777 corresponding disease targets; the disease targets and component targets were mapped, and topological analysis was used to determine the potential of Huangqin decoction in the treatment of DE. There were 156 targets, of which the top 20 genes were selected for GO and KEGG. The KEGG results showed that 134 pathways were enriched, which was partially consistent with the metabolic pathways of gut microbiota sequencing analysis. Conclusion. The results show that Huangqin decoction can inhibit the expression of inflammatory factors and related inflammatory pathways in intestinal epithelial cells, thereby regulating the structure of intestinal flora. Using picurst2 for functional prediction and metabolic pathway statistics, seven metabolic pathways were obtained consistent with gut microbiota sequencing, and the NOD-like receptor signaling pathway may be its potential molecular mechanism. These results help to understand the mechanism of Huangqin decoction on DE and provide the theoretical basis for further study of Huangqin decoction.
Ferroptosis-Related Long Noncoding RNAs as Prognostic Marker for Colon Adenocarcinoma
Authors: Qiu Yuting , Li Haobo , Zhang Qian , Qiao Xinwei , Wu Jing
Published: April 2022

Background. The incidence of colon adenocarcinoma (COAD) has been increasing over time. Although ferroptosis and long noncoding RNAs (lncRNAs) have been extensively reported to participate in the tumorigenesis and development of COAD, few studies have investigated the role of ferroptosis-related lncRNAs in the prognosis of COAD. Methods. Gene-sequencing and clinical data for COAD were obtained from The Cancer Genome Atlas database. The coexpression network was constructed using known ferroptosis-related genes. Cox and least absolute shrinkage and selection operator regression were used to screen ferroptosis-related lncRNAs with prognostic value and to identify a predictive model of COAD. Patients with COAD were divided into low- and high-risk groups according to their risk score. Cases of COAD in the International Cancer Genome Consortium database were included as the testing cohort. Results. In total, nine lncRNAs (LINC02381, AC105219.1, AC009283.1, LINC01011, ELFN1-AS1, EIF3J-DT, NKILA, LINC01063, and SNHG16) were considered prognostic factors for COAD. Then, a risk score model was established. The overall survival rate of COAD patients was negatively associated with the risk score. Kaplan–Meier analyses in the original and testing cohorts showed similar results. The expression of the lncRNAs in tissue was consistent with the risk score, and the relationship with tumor mutation burden, immunity, and drug sensitivity presented a marked link between the signature and COAD. A nomogram was established for clinical applications. Conclusions. Nine ferroptosis-related lncRNAs and the established signature have a certain predictive value for prognosis of COAD patients and can be used as potential research targets for exploring treatment of COAD.
Preoperative 3D Reconstruction Model in Slow Mohs Surgery for Dermatofibrosarcoma Protuberans
Authors: Huang Jia , Zhou Xiaobo , Ai Songtao , Chen Jun , Yang Jun , Sun Di
Published: April 2022

Dermatofibrosarcoma protuberans (DFSP) is a type of skin cancer that is extremely rare. Its standard treatment is either surgical wide-local excision (WLE) or Mohs micrographic surgery (MMS). Which method has the lowest recurrence rate is unknown. Dermatofibrosarcoma protuberans is an uncommon soft tissue sarcoma with a high propensity for recurrence. It has always remained a clinical challenge. More technology is needed to treat the disease. We reviewed our cases and published experience and evaluated whether 3D modeling could precisely define tumor morphological characteristics and assist excision in slow Mohs surgery. There were 18 dermatofibrosarcoma protuberan cases enrolled. They were treated in Shanghai Ninth People’s Hospital from 2014 to 2019. All the 18 included patients presented with primary disease and no metastasis. All subjects had undergone thorough imaging examinations including CT and MRI. The 3D tumor reconstruction models were created for their tumors. We precisely estimated tumor boundaries and sizes according to those 3D models. Afterward, patients underwent slow Mohs surgery and surgical repair of tissue defects following tumor resection. The 3D tumor reconstruction models were successfully established. The predicted tumor volumes were measured in all 18 cases. The average volume was 38.5 cm3 (range: 8.4 cm3–183.6 cm3), which allowed for accurately locating the tumor. Tumors were completely removed in one stage of slow MMS surgery. In the second-stage surgery, the defects were repaired by different surgical methods including direct soft tissue closure, skin grafting, local flaps, or free flaps. Most patients experienced no significant complications. This practice indicated that the combination of a 3D reconstruction model and slow Mohs surgery achieves more precise and complete DFSP resection to decrease the recurrence rate.
[Retracted] Analysis of Bioelectrical Impedance Spectrum for Elbow Stiffness Based on Hilbert–Huang Transform
Authors: Gao Guodong , Zhang Ping , Xu Bin , Zhang Xiaogang , Yang QuanZeng , Wang Rong , Han ShuHuan , Quan Zhen
Published: April 2022

With the advent of posttraumatic elbow rehabilitation, prevention of elbow stiffness has become a key part of the development of sports medicine. In order to clarify the time point of joint movement after internal fixation to the elbow and to provide a mechanical model for individualized diagnosis. This paper uses electromagnetic wave detection technology to quickly detect the bioelectrical impedance signal of the patient's lesion location, then passes the message to the upper control system for processing, summarizes the improved Hilbert–Huang transform to deep learning, and deep learning algorithms and computer technology are used to mine the bioelectrical impedance signal of the elbow joint. The simulation and human experiment results show that bioelectrical impedance signals can clarify the pathogenesis of elbow joint stiffness and the relationship between rehabilitation treatment time and duration. It has the advantages of low cost, high fitting accuracy, strong robustness, and noninvasiveness.
Proteomics Reveals Molecular Changes in Insomnia Patients with More Dreams
Authors: Liu Tao , Zhang Xingping , Wang Guanying , Liu Xin , Liang Zhengting , Ren Xiaojuan , Chen Chen , Yan Deqi , Zhang Wenhui
Published: April 2022

Background. Insomnia is a sleep disorder and the cause of many healthy problems. However, there are few studies on patients with insomnia and dreaminess at present. Therefore, this study is aimed at exploring the pathological molecular mechanisms and potential diagnostic and therapeutic targets related to insomnia patients with more dreams. Methods. Sleep characteristics of 36 primary insomnia patients with more dreams and 36 well sleeping participants were assessed using polysomnography (PSG) and Pittsburgh Sleep Quality Index (PSQI). Serum samples from 9 insomnia patients and 9 controls were randomly selected for proteomic detection. Differentially expressed proteins (DEPs) between the two groups were identified; enrichment analysis and PPI network were performed. The top 10 most connected proteins in the PPI network were subjected to targeted drug prediction and screened key proteins. Proteins with targeted drugs were recognized as key proteins and subjected to ELISA detection. Results. Insomnia patients had a distinct REM behavior disorder signature compared with controls. Proteomic sequencing identified 76 DEPs. Enrichment analysis found that DEPs were significantly enriched in the complement and coagulation cascades. Metabolic responses were also activated in insomnia patients. Among the hub proteins screened in the PPI network, APOA1, APOB, F2, and SPARC may be targeted by many herbal medicines and considered as key proteins. ELISA assays validated their differential expression between insomnia and controls. Conclusion. In this study, we identified the potential key proteins of insomnia patients with more dreams. The pathological process may associate with inflammation and metabolic response. These results provide molecular targets for diagnostic and therapeutic targets. The results of our analysis suggest that the expression changes of key proteins have a good predictive diagnostic role for the occurrence of insomnia with more dreams in patients.
Multimedia Security Situation Prediction Based on Optimization of Radial Basis Function Neural Network Algorithm
Authors: Chen Gan
Published: April 2022

Aiming at the problem of prediction accuracy in network situation awareness, a network security situation prediction method based on a generalized radial basis function (RBF) neural network is proposed. This method uses the K-means clustering algorithm to determine the data center and expansion function of the RBF and uses the least-mean-square algorithm to adjust the weights to obtain the nonlinear mapping relationship between the situation value before and after the situation and carry out the situation prediction. Simulation experiments show that this method can obtain situation prediction results more accurately and improve the active security protection of network security. Compared with the PSO-RBF model, AFSA-RBF model, and IAFSA-RBF model, the maximum relative error and minimum relative error of the IAFSA-PSO-RBF model are reduced by 14.27%, 8.91%, and 32.98%, respectively, and the minimum relative error is reduced by 1.69%, 12.97%, and 0.61%, respectively. This shows that the IAFSA-PSO-RBF model has reduced the prediction error interval, and the average relative error is 5%. Compared with the other three models, the accuracy rate is improved by more than 5%, and it has met the requirements for the prediction of the network security situation.
Establishment and Analysis of Hypoxia-Related Model in Oral Squamous Cell Carcinoma
Authors: Lu Xingwei , Liu Xingguang
Published: April 2022

Oral squamous cell carcinoma (OSCC) is one of the most common cancers in the world. Hypoxia is closely related to immunity in tumor microenvironment and also affects the prognosis of patients. However, there is still a lack of articles related to tumor hypoxia in oral squamous cell carcinoma. Therefore, we aimed to develop a hypoxia model for future application in patient prognosis analysis and immunotherapy. The transcriptome and survival information of OSCC were downloaded from GEO database. The Cox regression model of the lasso method was used to identify prognostic genes and develop gene characteristics based on hypoxia immunity. According to the median risk value, the patients were divided into high-risk group and low-risk group. Then, the estimated algorithm was used to estimate the relationship between hypoxia and immune status. At the same time, we evaluated the correlation and expression differences of immune-related genes between different risk groups. By using the lasso model, we identified two genes, including PFKP and SERPINE1, to construct gene signatures for risk stratification. We observed that both genes were highly expressed in the high-risk group, which was not conducive to the prognosis of the tumor. In addition, in the analysis of the degree of immune infiltration, we observed that there were differences in the content of a variety of immune cells between the two groups. It can be seen that there were great differences in the immune cells constituting the tumor microenvironment in oral squamous cell carcinoma. There remain significant differences in the expression levels of multitudinous immune-related genes. These immune-related genes include CCL chemokines, Chemokine (C-X-C motif) ligand (CXCL), CD antigens, HSP family, interferon family, and interleukin family. The hypoxia-immune-based gene signature represents a promising tool for risk stratification tool in oral squamous cell carcinoma cancer. It might serve as a prognostic classifier for clinical decision-making regarding individualized prognostication and treatment and follow-up scheduling.
English Multimode Production and Usage by Artificial Intelligence and Online Reading for Sustaining Effectiveness
Authors: Zhao Qin , Nazir Shah
Published: April 2022

In order to develop oral English and comprehensive skills, reading and writing English are more important. Artificial Intelligence (AI) plays a vital role in numerous fields of our lives, such as education, healthcare, businesses, and many others. In a short time, AI has become one of the most developed disciplines in educational technology and brings a lot of enhancement to the education system. In particular, AI has aided in the presentation of novel schooling and knowledge approaches that are currently being investigated in several sectors. Different AI-based apps, such as Chatbots, Robotic Assistants, Vidreader, Seeing AI, Classcraft, and 3D holograms, have been developed to help teachers and students take advantage of and enhance the educational system. Intelligent instruction methods and sentimentalized artificial learning assistance are given special study. The education industry’s main goal and desire is to create an intelligent education system, which has been made feasible by the development of teaching assistant robots, smart classrooms based on AI, and English teaching support, among other things. AI tools can now be used in all stages of learning to further improve the educational system. Students and teachers in COVID-19 have carried out their education and teaching in a variety of methods online. Learning, including virtual learning, can be done so that citizens do not lose their knowledge. This study has presented a detailed overview of the approaches and mechanisms used in the literature and then combined the literature evidence in the form of different analyses. This review will help researchers to develop and formulate new solutions in the field.
Integrated Multichip Analysis and WGCNA Identify Potential Diagnostic Markers in the Pathogenesis of ST‐Elevation Myocardial Infarction
Authors: Liang Yingliang , Wang Wandang , Huang Qiufang , Chen Hui
Published: April 2022

Background. ST‐elevation myocardial infarction (STEMI) is a myocardial infarction (MI) with ST‐segment exaltation of electrocardiogram (ECG) caused by vascular occlusion of the epicardium. However, the diagnostic markers of STEMI remain little. Methods. STEMI raw microarray data are acquired from the Gene Expression Omnibus (GEO) database. Based on GSE60993 and GSE61144, differentially expressed genes (DEGs) are verified via R software, and key modules associated with pathological state of STEMI are verified by weighted correlation network analysis (WGCNA). Take the intersection gene of key module and DEGs to perform the pathway enrichment analyses by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Construct the protein‐protein interaction (PPI) network by Cytoscape. Then, select and identify the diagnostic biomarkers of STEMI by least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine‐recursive feature elimination (SVM‐RFE) algorithms. Finally, assess the infiltration of immune cells of STEMI by CIBERSORT and analyze the correlation between diagnostic markers and infiltrating immune cells. Results. We get 710 DEGs in the STEMI group and 376 genes associated with STEMI in blue module. 92 intersection genes were concentrated in 30 GO terms and 2 KEGG pathways. 28 hub genes involved in the development of STEMI. Moreover, upregulated ALOX5AP (AUC = 1.00) and BST1 (AUC = 1.00) are confirmed as diagnostic markers of STEMI. CD8+T cells, regulatory T (Treg) cells, resting natural killer (NK) cells, M0 macrophages, resting mast cells, and neutrophils are related to the procession of STEMI. Moreover, ALOX5AP and BST1 are positively related to resting NK cells, M0 macrophages, and neutrophils, while ALOX5AP and BST1 are negatively related to CD8+ T cells, Treg cells, and resting mast cells. Conclusion. ALOX5AP and BST1 may be the diagnostic markers of STEMI. Immune cell infiltration plays a key role in the development of STEMI.