bluuebunny/crossref_metadata_2025 huggingface dataset:
Mixed aortic stenosis and regurgitation: a clinical conundrum
Authors: Nedadur Rashmi , Belzile David , Farrell Ashley , Tsang Wendy
Published: May 2022

Mixed aortic stenosis (AS) and aortic regurgitation (AR) is the most frequent concomitant valve disease worldwide and represents a heterogeneous population ranging from mild AS with severe AR to mild AR with severe AS. About 6.8% of patients with at least moderate AS will also have moderate or greater AR, and 17.9% of patients with at least moderate AR will suffer from moderate or greater AS. Interest in mixed AS/AR has increased, with studies demonstrating that patients with moderate mixed AS/AR have similar outcomes to those with isolated severe AS. The diagnosis and quantification of mixed AS/AR severity are predominantly echocardiography-based, but the combined lesions lead to significant limitations in the assessment. Aortic valve peak velocity is the best parameter to evaluate the combined haemodynamic impact of both lesions, with a peak velocity greater than 4.0 m/s suggesting severe mixed AS/AR. Moreover, symptoms, increased left ventricular wall thickness and filling pressures, and abnormal left ventricular global longitudinal strain likely identify high-risk patients who may benefit from closer follow-up. Although guidelines recommend interventions based on the predominant lesion, some patients could potentially benefit from earlier intervention. Once a patient is deemed to require intervention, for patients receiving transcatheter valves, the presence of mixed AS/AR could confer benefit to those at high risk of paravalvular leak. Overall, the current approach of managing patients based on the dominant lesion might be too reductionist and a more holistic approach including biomarkers and multimodality imaging cardiac remodelling and inflammation data might be more appropriate.
A non-invasive 13CO2 breath test detects differences in anabolic sensitivity with feeding and heavy resistance exercise in healthy young males: a randomized control trial
Authors: Mazzulla Michael , Hodson Nathan , West Daniel W.D. , Kumbhare Dinesh A. , Moore Daniel R.
Published: May 2022

There are limited tools to measure anabolic sensitivity non-invasively in response to acute physiological stimuli, which represents a challenge for research in free-living settings and vulnerable populations. We tested the ability of a stable isotope breath test to detect changes in leucine oxidation (OX) and leucine retention (intake–OX) across a range of anabolic sensitivities. Healthy males ingested a beverage containing 0.25 g·kg−1 protein and 0.75 g·kg−1 carbohydrate with the leucine content enriched to 5% with l-[1-13C]leucine at rest (FED) or after a bout of resistance exercise (EXFED), with a parallel group consuming only the tracer (FAST). Concurrent primed-constant infusions of l-[5,5,5-2H3]leucine revealed high peripheral bioavailability for FED (∼81%), EXFED (∼80%), and FAST (∼117%). After beverage ingestion, whole-body protein synthesis was greater in FED and EXFED than FAST. OX was greater in FED and EXFED than FAST, with OX lower in EXFED than FED. Leucine retention demonstrated expected physiological differences in anabolic sensitivity (EXFED > FED > FAST). We demonstrated that a non-invasive breath test based on an amino acid (leucine) that is preferentially metabolized in peripheral (muscle) tissues can detect differences in anabolic sensitivity. Future studies could examine this test within a variety of populations experiencing muscle growth or atrophy. This study was registered as a Clinical Trial at ClinicalTrials.gov (no. NCT04887727).

Novelty:

An oral l-[1-13C]leucine breath test can detect greater anabolic sensitivity after feeding and resistance exercise. This tool may be applied in growing (e.g., children) or wasting (e.g., aging) populations where invasive procedures are not possible.
Free boson realization of the Dunkl intertwining operator in one dimension
Authors: Vinet Luc , Zhedanov Alexei
Published: May 2022

The operator that intertwines between the [Formula: see text]-Dunkl operator and the derivative is shown to have a realization in terms of the oscillator operators in one dimension. This observation rests on the fact that the Dunkl intertwining operator maps the Hermite polynomials on the generalized Hermite polynomials.
The upgrade of the CMS electromagnetic calorimeter: Future prospects for precision timing and energy measurements at the HL-LHC
Authors: Biino Cristina ,   for the CMS Collaboration
Published: May 2022

The Compact Muon Solenoid (CMS) electromagnetic calorimeter (ECAL) is a high granularity lead tungstate crystal calorimeter operated at the CERN LHC. The future High Luminosity upgrade of the LHC will provide unprecedented instantaneous and integrated luminosity with up to 200 collisions per bunch-crossing [pileup (PU)]. The CMS detector is therefore undergoing an extensive upgrade program to prepare for these demanding conditions. In the ECAL barrel region, the crystals will continue to perform well. The Avalanche Photodiodes (APDs) detecting the scintillation light will also continue to be operational, with some increase in noise that will be mitigated by reducing the operational temperature. Nonetheless, the entire readout and trigger electronics will need to be replaced to cope with the harsh conditions and increased trigger latency requirements. We plan a 25-fold improved readout granularity and a factor 4 sampling rate increase. The timing precision is used in important physics measurements and it is speculated that further improved time information could be exploited for PU mitigation and for the photons assignment to the correct collision vertex. As presented in this paper the endcaps region calorimeter needs to be replaced due to irrecoverable radiation damage of the crystals in the forward region. The upgraded ECAL will preserve the energy resolution, and will improve on time resolution for photons and electrons. In this paper, the ongoing R&D activities for the ECAL barrel readout electronics upgrade are presented.
Asymptotic analysis of deformation behavior in high-contrast fiber-reinforced materials: Rigidity and anisotropy
Authors: Engl Dominik , Kreisbeck Carolin , Ritorto Antonella
Published: May 2022

We identify the restricted class of attainable effective deformations in a model of reinforced composites with parallel, long, and fully rigid fibers embedded in an elastic body. In mathematical terms, we characterize the weak limits of sequences of Sobolev maps whose gradients on the fibers lie in the set of rotations. These limits are determined by an anisotropic constraint in the sense that they locally preserve length in the fiber direction. Our proof of the necessity emerges as a natural generalization and modification of the recently established asymptotic rigidity analysis for composites with layered reinforcements. However, the construction of approximating sequences is more delicate here due to the higher flexibility and connectedness of the soft material component. We overcome these technical challenges by a careful approximation of the identity that is constant on the rigid components, combined with a lifting in fiber bundles for Sobolev functions. The results are illustrated with several examples of attainable effective deformations. If an additional partial second-order regularization is introduced into the material model, only rigid body motions can occur macroscopically.
Optimal Feature Selection with Weight Optimised Deep Neural Network for Incremental Learning-Based Intrusion Detection in Fog Environment
Authors: Abdussami Aftab Alam , Farooqui Mohammed Faizan
Published: May 2022

Fog computing acts as an intermediate component to reduce the delays in communication among end-users and the cloud that offer local processing of requests among end-users through fog devices. Thus, the primary aim of fog devices is to ensure the authenticity of incoming network traffic. Anyhow, these fog devices are susceptible to malicious attacks. An efficient Intrusion Detection System (IDS) or Intrusion Prevention System (IPS) is necessary to offer secure functioning of fog for improving efficiency. IDSs are a fundamental component for any security system like the Internet of things (IoT) and fog networks for ensuring the Quality of Service (QoS). Even though different machine learning and deep learning models have shown their efficiency in intrusion detection, the deep insight of managing the incremental data is a complex part. Therefore, the main intent of this paper is to implement an effective model for intrusion detection in a fog computing platform. Initially, the data dealing with intrusion are collected from diverse benchmark sources. Further, data cleaning is performed, which is to identify and remove errors and duplicate data, to create a reliable dataset. This improves the quality of the training data for analytics and enables accurate decision making. The conceptual and temporal features are extracted. Concerning reducing the data length for reducing the training complexity, optimal feature selection is performed based on an improved meta-heuristic concept termed Modified Active Electrolocation-based Electric Fish Optimization (MAE-EFO). With the optimally selected features or data, incremental learning-based detection is accomplished by Incremental Deep Neural Network (I-DNN). This deep learning model optimises the testing weight using the proposed MAE-EFO by concerning the objective as to minimise the error difference between the predicted and actual results, thus enhancing the performance of new incremental data. The validation of the proposed model on the benchmark datasets and other datasets achieves an attractive performance when compared over other state-of-the-art IDSs.
Investigating the Effect of Motives, Social Capital, and Human Capital on Commercialization of Self-Developed Products by User Innovators: A Study of a Crowdfunding Platform
Authors: Ahvari Hooman , Ekhlassi Amir , McDonagh Joe
Published: May 2022

User innovators follow multiple diffusion and adoption pathways for their self-developed innovations. Users may choose to commercialize their self-developed products on the marketplace by becoming entrepreneurs. Few studies exist that focus on understanding personal and interpersonal factors that affect some user innovators’ entrepreneurial decision-making. Hence, this paper focuses on how user innovators make key decisions relating to opportunity recognition and evaluation and when opportunity evaluation leads to subsequent entrepreneurial action in the entrepreneurial process. We conducted an exploratory study using a multi-grounded theory methodology as the user entrepreneurship phenomenon embodies complex social processes. We collected data through the netnography approach that targeted 18 entrepreneurs with potentially relevant differences through crowdfunding platforms. We integrated self-determination, human capital, and social capital theory to address the phenomena under study. This study’s significant findings posit that users’ motives are dissatisfaction with existing goods, interest in innovation, altruism, social recognition, desire for independence, and economic benefits. Besides, use-related experience, product-related knowledge, product diffusion, and iterative feedback positively impact innovative users’ entrepreneurial decision-making.
Determinants of Innovative Work Behavior and Job Performance: Moderating Role of Knowledge Sharing
Authors: Faris Hussain Muhammad , Hanifah Haniruzila , Vafaei-Zadeh Ali , Abdul Halim Hasliza
Published: May 2022

The purpose of this study is to look into the elements that influence innovative work behavior and job performance while taking into account the moderating role of information sharing. This research sampled middle-level management employees from food and beverage manufacturing sector employing the purposive sampling technique. A survey questionnaire technique has been used to collect data from 161 respondents in three months. The results revealed that management support has no significant effect on innovative work behavior. Furthermore, information sharing has no impact on the relationship between management support and co-worker support, as well as innovative work behavior. However, innovative work behavior moderates the relationships between co-workers support, job autonomy, and job performance. The results of this study may enhance the knowledge of manufacturing SMEs on innovative work behavior and job performance. The findings of this research may improve manufacturing SMEs’ understanding of innovative work behavior and job performance.
Kick-Starting Roadmapping Implementation in Corporate Settings: Lessons Learned From IHI Corporation
Authors: Hirose Yuta , Phaal Robert , Farrukh Clare , Gerdsri Nathasit , Lee Sungjoo , Yamaoka Hiroto
Published: May 2022

Roadmapping is an established visual mapping method used widely in a variety of organizations. It navigates technology and strategic planning in a structured manner for improved strategy development and decision-making. However, roadmapping implementation can be challenging particularly when initiating in corporate organizations due to the broad range of stakeholder involvement. This paper examines the background history and lessons learned from IHI Corporation, a leading Japanese heavy industry manufacturer, to provide insights regarding how roadmapping implementation can be initiated effectively in corporate settings. Using the roadmapping maturity model for analysis and discussion, this paper contributes to both industrial practice and academic understanding of roadmapping implementation maturity.
Driving Forces of Adaptation Decision and Strategies to Climate-Related Events: Case on Farming Households in South–West Coastal Bangladesh
Authors: Kabir Kazi Humayun , Khan Md. Ayatullah , Hasan Kamrul , Aftab Sharmin
Published: May 2022

This research aimed to identify the factors that influence farming households’ decisions on adaptation strategies and the highest priority strategy in South-Western coastal Bangladesh. Seven unions in Koyra Upazila were surveyed, and 60 households were interviewed from each union. The binary logit and multinomial logit models were used to analyse the data. The findings indicate that 58.6% of farming households adopted climate-related adaptation strategies, with seasonal migration being the most top priority. Similarly, the data suggested that years of education of the household heads, number of household members, land tenure, farming experience, climatic event risk perception, and farmer-to-farmer extension influenced both farming households’ adaptation decisions and the likelihood of choosing the highest priority adaptation strategies. Public–private partnerships (PPPs) can integrate funds, knowledge, and power structures. It can also balance expenses, liabilities, and opportunities. So, the development of PPPs may promote suitable adaptation strategies.
Optimization and Application of Random Forest Algorithm for Applied Mathematics Specialty
Authors: Li Wei
Published: May 2022

For unbalanced data classification, RF (Random forest) algorithm will cause problems such as poor classification performance and a large DT scale. With the advent of the era of big data, RF algorithms should have the ability to process large-scale data. Aiming at the problem that RF cannot handle unbalanced data well, this paper improves the feature selection method built in RF and proposes a new feature selection algorithm. On the basis of feature importance ranking, randomness is introduced to ensure the strength of each tree and reduce the correlation between trees. In the extended transform data set, the sensitivity of the RF model has exceeded 0.8, and that of other models has increased to about 0.65. The prediction accuracy of the centralized RF model for the company’s credit rating reached 100%, while the CART model misjudged companies C6 and C7, while the Logit model misjudged companies C3, C5, and C8. Experiments prove the extrapolation of the RF model and its excellent prediction ability. In the practical application of applied mathematics specialty, the RF optimization algorithm proposed in this study can well handle continuous variables and improve the classification accuracy of RF. This paper holds that the advantages of the RF algorithm in data processing and model performance will make it more widely used in the field of enterprise credit risk evaluation.
Optimal Design of a Novel Rescue Accessory with Spreading and Supporting Functions
Authors: Wang Chunrong , Xia Erdong , Wang Kun-Chieh , Xiong Changjiong
Published: May 2022

A novel rescue accessory with spreading and supporting functions is proposed to promote the performance of usage continuity and improve the rescue efficiency of traditional rescue devices. To optimally design this rescue device, we apply related working principles to analyze the motion of the whole device and its spreading mechanism. In optimization, three important parameters of the spreading width, the jaw rotation angle, and the spreading force are simultaneously chosen as the objective functions. Meanwhile, specific mathematical models are established for estimating the performance of the novel rescue accessory. Then, an improved algorithm based on the nondominated sorting genetic algorithm (INSGA-II) is proposed to solve the above multiobjective optimization problem of the rescue accessory. The INSGA-II optimizes the population diversity, the crossover operator, and the mutation operation in calculation procedures. Furthermore, it can dynamically adjust the crowded distance and enhance the global as well as the local searching capabilities of INSGA-II. Results from computational tests show that INSGA-II has the best performance among other popular algorithms. More importantly, comparison results show that the compromise solution of the Pareto front for the above three objective functions obtained by INSGA-II is superior to those by other algorithms.
Analysis of X-Ray Beam Hardening Correction Method Relying on Cloud Computing Big Data
Authors: Liu Yanxiu , Li Ye , Jiang Sheng , Zhao Peng , Qin Xulei , Liu Yanyang
Published: May 2022

Regarding the correction of X-ray beam hardening in the current CT imaging system, the traditional method will cause the overlapping of images during use, which will gradually harden the beam, and the image reconstructed by the imaging system will gradually become “cup-shaped” or “striped.” “False images” seriously degrade the quality of the images, while causing more interfering diagnostic problems. In this paper, the cloud computing big data analysis algorithm is applied to the X-ray beam hard correction process. According to the transition energy and its energy absorption, the X-ray beam is used as the cut-in point, and the relationship between the attenuation coefficient of similar materials and the X-ray energy is used to remove the artifact image in the initial image reconstruction process to obtain a clear corrected image. Meanwhile, according to the thickness and gray value of the X-ray penetrable object, the result of fitting using a polynomial function is calculated, and an accurate line fitting can be completed for data with smaller coordinates. Finally, the experimental study shows that the cloud computing big data analysis proposed in this paper can detect X-ray beams in real time. This method uses optical receivers to achieve high noise sensitivity to X-rays, and in long-distance transmission scenarios, the bandwidth of communication transmission can be maximized, and different types of formats can be used to complete modulation for different X-rays. Therefore, X-ray beam hardening correction technology has better advantages and the market application scenarios compared with other technologies.
Construction of an Artificial Intelligence Writing Model for English Based on Fusion Neural Network Model
Authors: Hsiao Meijin , Hung Maosheng
Published: May 2022

This paper presents an in-depth study and analysis of the model of English writing using artificial intelligence algorithms of neural networks. Based on word vectors, the unsupervised disambiguation, and clustering of multimedia contexts extracted from massive online videos, the disambiguation accuracy reaches over 0.7, and the resulting small-scale multimedia context set can cover up to 90% of vocabulary learning tasks; user experiments show that the multimedia context learning system based on this method can improve the effectiveness and experience of ESL vocabulary learning, as well as the long-term word sense memory of learners. The results are 30% better. Based on the dependency grammatical relations and semantic metrics of collocations on a large-scale professional corpus, we established a collocation intention description and retrieval method in line with users’ linguistic cognition and doubled the usage rate of collocation retrieval on the actual deployment system after half a year, becoming a user “sticky” ESL writing aid, and further defined style. Dictionaries only provide basic lexical definitions, and, even if supported by example sentences, they still cannot meet the needs of ESL authors in terms of expressive accuracy and richness. However, the current machine translation is based on the black box deep neural network construction, and its translation process is not understandable and interactive. Among the three algorithmic models constructed in this paper, the multitask learning model outperforms the conditional random field model and the LSTM-CRF model because the multitask learning model with auxiliary tasks solves the problem of sparse data to a certain extent, allowing the model to be trained more adequately in the case of uneven label distribution, and thus performs better than other models in the task of grammatical error detection.
Adjuvant Chemotherapy in Node-Negative Advanced Gastric Cancer Patients
Authors: Su Jian-wei , Zeng Ya-ting , Luo Shu-ai , Sun Yu-ying , Huang Chun-yu
Published: May 2022

Currently, there is still controversy on postoperative adjuvant chemotherapy for node-negative advanced gastric cancer. Herein, we sought to evaluate the role of postoperative adjuvant chemotherapy in these patients. We retrospectively analyzed the clinical and pathological characteristics of 363 node-negative advanced gastric cancer patients in our hospital from 1996 to 2007 who underwent gastrectomy and D2 lymphadenectomy. We compared the survival rate of the surgery-only group with that of the adjuvant chemotherapy treatment group. The 5-year survival rates of patients in the surgery-only group and the chemotherapy treatment group were 70.7% and 73.8%, respectively. There was no significant difference in the survival rate between patients receiving postoperative chemotherapy and patients not receiving chemotherapy (

P
=
0.328

). However, postoperative chemotherapy treatment significantly increased the survival rate of pT4aN0M0 patients (

P
=
0.020

), although it did not exert a direct effect on the survival rate in pT2N0M0 and pT3N0M0 patients (

P
=
0.990

and

P
=
0.895

). We also summarized and analyzed the side effects and safety of postoperative adjuvant chemotherapy. The rate of chemotherapy-related adverse events was 79.9%. Although 61 (36.1%) patients had to adjust their chemotherapy dose, no patient died from side effects. In conclusion, postoperative chemotherapy treatment is safe but did not show a direct impact on the survival rate of the node-negative advanced gastric cancer patients. However, pT4aN0M0 patients can benefit from postoperative adjuvant chemotherapy after undergoing D2 radical resections.
Research on the Image Description Algorithm of Double-Layer LSTM Based on Adaptive Attention Mechanism
Authors: Qin Cifeng , Gong Wenyin , Li Xiang
Published: May 2022

Image text description is a multimodal data processing problem in the computer field, which involves the research tasks of computer vision and natural language processing. At present, the research focus of image text description task is mainly on the method based on deep learning. The work of this paper is mainly focused on the imprecise description of visual words and nonvisual words in the description of image description tasks in the image text description. An adaptive attention double-layer LSTM (long short-term memory) model based on coding-decoding is proposed. Compared with the algorithm based on the adaptive attention mechanism based on the coding-decoding framework, the evaluation index BLEU-1 is improved by 1.21%. The METEOR was 0.75% higher and CIDEr was 0.55%, while the indexes of BLEU-4 and ROUGE-L were not as good as those of the original model, but the index was not different. Although it cannot surpass all the performance indicators of the original model, the description of visual words and nonvisual words is more accurate in the actual image text description.
Long Noncoding RNA SNHG16 Regulates the Growth of Human Lung Cancer Cells by Modulating the Expression of Aldehyde Dehydrogenase 2 (ALDH2)
Authors: Li Yan , Jiang Lifeng , Zhu Zhaocheng , Fu Binfan , Sun Xu , Jiao Yue
Published: May 2022

The involvement of long noncoding RNA (lncRNA) SNHG16 has been reported in several human cancers. Notwithstanding, the role of lncRNA SNHG16 is yet largely unknown in human lung cancer. Consequently, this study was undertaken to investigate the role and therapeutic potential of SNHG16 in human lung cancer. The results showed a significant (

P
<
0.05

) transcriptional upregulation of SNHG16 in lung cancer tissues and cell lines. However, downregulation of SNHG16 resulted in significant (

P
<
0.05

) inhibition of lung cancer A549 and SK-LU-1 cell proliferation. DAPI and annexin V/PI assays revealed apoptosis to be responsible for inhibition of cell proliferation and colony formation observed upon SNHG16 knockdown. This was accompanied by enhancement of Bax and suppression of Bcl-2 expression in A549 and SK-LU-1 cells. Transwell assays revealed that silencing of SNHG16 also significantly (

P
<
0.05

) inhibited migration and invasion of A549 and SK-LU-1 cells. Bioinformatic analysis revealed that SNHG16 interacted with ALDH2 to exert its effects in human lung cancer cells. The expression of ALDH2 was found to be significantly (

P
<
0.05

) suppressed in human lung cancer tissues and cell lines. Overexpression of ALDH2 inhibited the proliferation and colony formation of the A549 and SK-LU-1 cells. However, silencing of ALDH2 could avoid the tumor-suppressive effects of SNHG16 knockdown. Finally, SNHG16 silencing was also found to inhibit in vivo tumor growth. Collectively, the study unveils the molecular role of SNHG16 in regulating the development of lung cancer by interacting with ALDH2.
Research on Distributed Multisensor Spectrum Semantic Sensing and Recognition in Internet of Things Environment
Authors: Liu Xiguo , Zhang Jing , Liu Min , Mao Zhongyang , Hou Changbo
Published: May 2022

The development of 5G technology has brought about a new era of Internet of Thing (IoT), and at the same time, electromagnetic spectrum monitoring and sensing have also ushered in huge challenges. Digital modulation recognition technology is an important content of electromagnetic spectrum sensing. In the increasingly complex wireless communication transmission environment, especially in noncooperative communication, it becomes more and more difficult to receive target signals and accurately extract effective semantic information from diverse modulation signals of the electromagnetic spectrum. At this stage, with the rapid development of network information and wireless communication technology, within a prescribed distance, the IoT built by many sensors has attracted wide attention from people in related fields. This paper proposes a distributed collaborative sensing spectrum semantic recognition architecture for communication signals based on feature fusion. Perform wireless communication and transmission between multiple sensors to form a self-organizing network to cooperatively sense signal semantic information, and extract the signal features of each sensor in the distributed network structure. Finally, the extracted sensor features are semantically analyzed and modeled, and the effective features are fused to complete the entire perception and recognition process. Even if the channel environment of a small number of receiving nodes deteriorates in a complex transmission environment, the signal quality features can still be accurately extracted, the classification and recognition effect like or higher than the best channel state performance can be achieved, and the fault tolerance of the system can be effectively improved. It can also enhance the performance of spectral semantic information sensing and recognition in the IoT environment.
LncRNA LINC01116 Regulates the Proliferation, Migration, and Invasion of Cervical Cancer Cells by Targeting miR-744-5p
Authors: Wang Jing , Yue Lei , Shen Ye
Published: May 2022

Objective. To investigate the effects and potential molecular mechanisms of LncRNA LINC01116 on proliferation, migration, and invasion of cervical cancer cells. Method(s). The content of miR-744-5p and LINC01116 in cervical cancer cells HeLa, SiHa, and C33a was detected by RT-PCR, the proliferative activity and clone number of SiHa cells were determined by MTT and clone formation assay, the number of invaded and migrated cells was determined by Transwell assay, the expressions of Cyclin D1 and MMP-2 in cells were detected by Western blot, and the activity of luciferase detected by dual-luciferase reporting system verified the regulatory relationship between LINC01116 and miR-744-5p. Result(s). Compared with human normal cervical epithelial cells Ect1/E6E7, the content of LINC01116 in cervical cancer cells HeLa, SiHa, and C33a was increased significantly [(

1.04
±
0.12

) vs. (

3.34
±
0.38

)/(

4.48
±
0.45

)/(

3.59
±
0.45

)] (

P
<
0.05

), the content of miR-744-5p was decreased significantly [(

0.98
±
0.09

) vs. (

0.39
±
0.04

)/(

0.34
±
0.05

)/(

0.41
±
0.05

)] (

P
<
0.05

). Silencing LINC01116 could inhibit the protein expression of Cyclin D1 [(

0.68
±
0.06

) vs. (

0.31
±
0.04

)] and MMP-2 [(

0.74
±
0.08

) vs. (

0.34
±
0.05

)] in SiHa cells, and inhibition of cell proliferation [(

100.98
±
7.89

)% vs. (

67.26
±
6.14

)%], clone formation ability [(

180.31
±
11.34

) vs. (

53.56
±
6.18

)], migration [(

234.67
±
15.31

) vs. (

98.46
±
7.46

)], and invasion [(

134.83
±
9.12

) vs. (

62.44
±
5.34

)]. LINC01116 targeted and negatively regulates the expression of miR-744-5p. Overexpression of miR-744-5p could inhibit the proliferation of cervical cancer SiHa cells [(

101.64
±
4.89

)% vs. (

62.34
±
5.73

)%], clone formation ability [(

179.24
±
12.34

) vs. (

47.67
±
5.18

)], migration [(

241.06
±
16.64

) vs. (

84.52
±
7.57

)], and invasion [(

127.92
±
8.16

) vs. (

65.24
±
7.36

)]. Inhibition of miR-744-5p reversed the effects of silencing LINC01116 on SiHa cell proliferation [(

102.46
±
7.64

)% vs. (

146.72
±
12.23

)%], clone formation ability [(

47.64
±
6.20

) vs. (

104.26
±
8.64

)], migration [(

81.52
±
7.42

) vs. (

153.64
±
12.57

)], and invasion [(69.12 ± 5.56) vs (94.31 ± 7.44)]. Conclusion(s). LncRNA LINC01116 inhibits proliferation, migration, and invasion of SiHa cells by targeting miR-744-5p, and LINC01116 is a potential molecular target for cervical cancer.
Experimental Study on the Shear Strength of Loess Modified by Microbial Precipitation Technology
Authors: Wang Bing-Xia , Shi Li-Jun , Dong Jian-Hua , Wang Lu , Tian Wen-Tong
Published: May 2022

The shear strength (vertical pressure 50, 100, 200, and 300 kPa) and soil structure of the remolded loess with different proportions of the microbial cementing solution were measured and observed by the direct shear test and scanning electron microscopy (SEM).The results showed: (1) the increase in the microbial cementation solution led to the transformation of the stress-shear displacement curve from strain hardening to strain softening, with the transition interval of 26%∼34%. (2) With the increase in the microbial cementation solution, the change of cohesion showed a trend of “M,” the inflection point of the microbial cementation solution was 14%, 22%, and 34%, while the internal friction angle showed a law of “W,” where the inflection points of the internal friction angle were about 14%, 30%, 34%, and 38%, respectively. In the range of 18%∼30%, the cohesion and internal friction angle increase with an increase in the microbial cementation solution.(3) The change of cohesion is affected by the microbial mineralization saturation and the joint action of the occurrence state of Ca2+ and HCO3- in the microbial cementation solution. The change of internal friction angle is affected by the soil particle contact, the existing form of calcium carbonate formed by microbial precipitation, pore morphology, yield, and other forces. (4) By means of SEM, the distribution morphology of the soil particles and the contact microscopic images of the loess samples modified by microorganisms with different proportions were further verified to further verify the macroscopic changes of the shear strength of the modified loess samples cemented by microorganisms with different proportions. It provides a theoretical basis for the improvement of loess soil structure characteristics by microorganism calcium carbonate precipitation technology.
Traditional Pattern Feature Extraction and Cultural Creative Design Application Based on Multilevel Histogram Shape Segmentation
Authors: Liang Xin
Published: May 2022

Content-based stereo video and image retrieval relies on traditional pattern feature extraction and cultural creative design applications. In a 3D system, the 3D model is typically projected into a 2D point cloud, after which feature extraction takes place. A depth map is an image created by orthogonal projection of three-dimensional data or viewpoint matching, parallax calculation, and resampling into regular data based on depth value. In this paper, a multilevel histogram-based shape segmentation method is proposed. The threshold can be determined by analyzing the relationship between the peaks if there are several obvious peaks on the image histogram. This paper focuses on the use of traditional pattern feature extraction and cultural creative design based on the shape segmentation of multilevel histograms. Each algorithm can achieve good classification results in the experiment of rotation invariant similarity classification. The average accuracy of the HOD algorithm is 84.1%, the average accuracy of the RSDF algorithm is 86.2%, the average accuracy of the GIF algorithm is 81.3%, and the average accuracy of this paper’s algorithm is 89.9% when the rotation angle is 60–120°. The results show that this paper’s algorithm has a higher classification accuracy compared to the other algorithms and that it maintains a high average classification accuracy across all rotation angles, indicating that it is rotation invariant and robust. The histogram-based threshold method has the advantage of not requiring knowledge of the image’s prior information. The algorithm is easy to understand, straightforward, and quick. However, because the valley point in the histogram is not visible in images with low or high contrast between the target and the background area, it is difficult to select the threshold. The results of traditional pattern feature extraction can be improved using the retrieved pixels.
Ketamine Boluses Are Associated with a Reduction in Intracranial Pressure and an Increase in Cerebral Perfusion Pressure: A Retrospective Observational Study of Patients with Severe Traumatic Brain Injury
Authors: Dengler Bradley A. , Karam Oliver , Barthol Colleen A. , Chance Aaron , Snider Laura E. , Mundy Clare M. , Bounajem Michael T. , Johnson William C. , Maita Moustafa M. , Mendez-Gomez Paola M. , Seifi Ali , Hafeez Shaheryar
Published: May 2022

Background. Increased intracranial pressure (ICP) and hypotension have long been shown to lead to worse outcomes in the severe traumatic brain injury (TBI) population. Adequate sedation is a fundamental principle in TBI care, and ketamine is an attractive option for sedation since it does not commonly cause systemic hypotension, whereas most other sedative medications do. We evaluated the effects of ketamine boluses on both ICP and cerebral perfusion pressure (CPP) in patients with severe TBI and refractory ICP. Methods. We conducted a retrospective review of all patients admitted to the neurointensive care unit at a single tertiary referral center who had a severe traumatic brain injury with indwelling intracranial pressure monitors. We identified those patients with refractory intracranial pressure who received boluses of ketamine. We defined refractory as any sustained ICP greater than 20 mmHg after the patient was adequately sedated, serum Na was at goal, and CO2 was maintained between 35 and 40 mmHg. The primary outcome was a reduction in ICP with a subsequent increase in CPP. Results. The patient cohort consisted of 44 patients with a median age of 30 years and a median presenting Glasgow Coma Scale (GCS) of 5. The median reduction in ICP after administration of a ketamine bolus was −3.5 mmHg (IQR −9 to +1), and the postketamine ICP was significantly different from baseline (

p
<
0.001

). Ketamine boluses led to an increase in CPP by 2 mmHg (IQR −5 to +12), which was also significantly different from baseline (

p
<
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). Conclusion. In this single-institution study of patients with severe traumatic brain injury, ketamine boluses were associated with a reduction in ICP and an increase in CPP. This was a retrospective review of 43 patients and is therefore limited in nature, but further randomized controlled trials should be performed to confirm the findings.
Influence of the Branched Structure of Polyoxyethylene Units in Nonionic Surfactants on the Wettability of Anthracite: A Combined Modeling and Experimental Study
Authors: Chen Xuanlai , Yan Guochao , Xu Guang , Yang Xianglin , Li Jiajun , Bai Xuyang
Published: May 2022

Coal dust is a significant concern to the safety of coal mine operations. The wettability of coal can be effectively altered by adding surfactants to water. The development of high-efficiency dust suppressants is hampered by a lack of understanding of the microscopic interaction process between coal dust and surfactants. In this investigation, the influence of the branched structure of the polyoxyethylene unit in nonionic surfactants on the wettability of anthracite surfaces was evaluated by combining the modeling study and experimental research. The macromolecular model of Jincheng anthracite with 55 different components (C7730H3916O133N123S25) was constructed. Lauryl polyoxyethylene ether C12(EO)20 and Tween 20 were selected. The simulation results showed that due to the branched structure of polyoxyethylene, the surface of anthracite after adsorption by Tween 20 is more hydrophilic. Further analysis found that the adsorption configuration of Tween 20 is that the hydrophilic head group covers the hydrophobic tail chain, while the adsorption configuration of C12(EO)20 is that the hydrophobic tail chain covers the hydrophilic head group. The network structure formed by Tween 20 is relatively loose, and the surface is rougher. The network structure formed by C12(EO)20 is denser. Water molecules have a higher aggregation degree near Tween 20 and stronger permeability, and more hydrogen bonds are formed. The existence state of carbon and oxygen elements on the surface of modified coal was analyzed by XPS experiment, which confirmed the adsorption structure obtained by molecular simulation.
Research on Dynamic Programming Strategy of Bayesian Network Structure Learning
Authors: Di Ruohai , Li Ye , Li Tingpeng , Wang Peng , He Chuchao
Published: May 2022

Bayesian network structure learning based on dynamic programming strategy can be used to find the optimal graph structure compared with approximate search methods. The traditional dynamic programming method for Bayesian network structure learning is a depth-first-based strategy, which is inefficient. We proposed two methods to solve this problem. First, the dependency constraints were used to prune the process of calculating redundancy scores. The constraints were obtained by the conditional independence test from the observed data sets. However, it was difficult to guarantee the accuracy of the constraints, which may have led to a decrease in the accuracy of the method. Second, we proposed a breadth-first-based strategy, which enhanced efficiency greatly while also ensuring global optimality. Experimental results showed that on the standard network data sets, compared with the dynamic programming based on depth-first search (DFSDP) algorithm, dynamic programming based on constraints (CBDP) could reduce the average running time by 57.10% and that dynamic programming based on breadth-first search (BFSDP) could reduce the average running time by 50.02%. On the UCI data sets, compared with DFSDP, CBDP reduced the average running time by 40.71%, and BFSDP reduced the average running time by 81.78%.
Exploration on the Application Path of College English MOOCS Teaching under the Background of Internet of Things
Authors: Xu Daoping
Published: May 2022

While the development of information and communication technology has brought great changes to social industrial structure and economic development, it has also reshaped people’s cognition and thinking modes to a great extent and has paid attention to the field of education under the guidance of technology. The reform of education is taking place. According to the background of the Internet of things, the teaching quality evaluation index is reconstructed with the help of principal component analysis, support vector machine, and fuzzy algorithms, and the application performance and practical results of the improved teaching path are analyzed. The results show that the evaluation accuracy of the improved hybrid algorithm for teaching quality reaches more than 90%, and its evaluation time is far less than that of the SVM algorithm (10.23 s), and it has a good application effect on the evaluation of teaching quality. The increase of students’ satisfaction with the improved teaching quality has reached 5.8%, and their professional skills have improved by 6–25 points. After expert evaluation, it is found that the maximum satisfaction frequency of teaching content and teaching effect can reach 96.7% and 91.3%, respectively. Reconstructing the index system of the English path under the background of the Internet of things can better grasp students’ learning needs and integrate multiple methods to provide new research ideas for follow-up English teaching.
Simulation Analysis of Combined Mechanics of the Thrust Rotary Guide Drill
Authors: Zeng Bo , Zheng Youcheng , Wu Pengcheng , Zhou Feng , Huang Mei , Bai Yuxin , Gao Yangyang , Xia Chengyu
Published: May 2022

The force and deformation analysis of the bottom hole assembly (BHA) is the foundation and essential component of good trajectory control technology. Still, the BHA structure has complexity (multiangle), making it challenging to analyze the force and deformation of the BHA accurately. In this paper, we consider the factors such as well drilling parameters (drill pressure), drill combination structure parameters (multivariable cross section, stabilizer, and flexible short section diameter and position, etc.), a mechanical analysis model of the push-the-bit rotary steering tool is established based on the infinitesimal method and continuous beam-column theory. Taking the push-the-bit rotary steerable drilling assembly as an example, the influence of force and deformation of the rotary guided drilling assembly, such as drill pressure, stabilizer, and flexible short section parameters, was analyzed. The research results of this paper can solve the mechanical modeling problem of the variable cross section in the bottom drill combination, can deal with the combination of infinite multiple stiffnesses according to the actual situation, bring the analysis results closer to the real problem, and provide theoretical support for the design optimization of the bottom drill combination. In addition, edge computing can provide sufficient computing power for the calculation of this paper to ensure operational efficiency. It can realize the online real-time transmission of mechanical analysis results through powerful calculation examples and effectively guide the field operation.
Design of an Integrated Approach to Industrial Logistics Information Based on Supply Chain Management
Authors: Shang Yang
Published: May 2022

In recent years, with the continuous integration and development of manufacturing and intelligent technologies, the enhancement of the intelligence and informatization of production becomes an important issue in intelligent manufacturing. Industrial logistics systems are responsible for the transport of tools and raw materials for production and processing. Therefore, industrial logistics plays an essential role in improving the production efficiency of products. The value of the supply chain comes from the synergy of the various processes between various companies. The development of industrial logistics has effectively solved the issue of the inability of logistics to synchronize product and service flows in the supply chain and is an important part of the overall supply chain for the relevant enterprises. Now, the logistics industry is an important support industry for China’s economic development, and how to achieve rapid and stable development of the logistics industry is a key issue facing China that needs to be resolved. In addition, with the globalization of the economic environment today, enterprises are facing increasingly fierce competition. In this context, most companies are taking logistics informatization as an important factor for their success. Meanwhile, with the rapid development of the information industry, new information technologies are emerging. The application of new information technologies can improve the competitiveness of enterprises to a certain extent. After all, an important feature of modern industry is the organic integration of industry and services to promote the modernization of industry. The logistics industry, as a leading service industry, has already penetrated all aspects of the industry. However, the industry has not yet developed a comprehensive approach to the integration of industrial logistics information. The selection of a model based on unilateral consideration of transport costs and other indicators is no longer appropriate to the current situation. Therefore, it is of practical significance to study the integration of logistics information in the supply chain environment. Based on supply chain management, this research explores and designs a relevant approach to the integration of industrial logistics information, thus providing some suggestions for the direction of industrial logistics.