bluuebunny/crossref_metadata_2025 huggingface dataset:
A Digital Calibration Source for 21 cm Cosmology Telescopes
Authors: Bhopi Kalyani , Tyndall Will , Sanghavi Pranav , Bandura Kevin , Newburgh Laura , Gallicchio Jason
Published: August 2022

Foreground mitigation is critical to all next-generation radio interferometers that target cosmology using the redshifted neutral hydrogen 21 cm emission line. Attempts to remove this foreground emission have led to new analysis techniques as well as new developments in hardware specifically dedicated to instrument beam and gain calibration, including stabilized signal injection into the interferometric array and drone-based platforms for beam mapping. The radio calibration sources currently used in the literature are broad-band incoherent sources that can only be detected as excess power and with no direct sensitivity to phase information. In this paper, we describe a digital radio source which uses Global Positioning Satellite (GPS) derived time stamps to form a deterministic signal that can be broadcast from an aerial platform. A copy of this source can be deployed locally at the instrument correlator such that the received signal from the aerial platform can be correlated with the local copy, and the resulting correlation can be measured in both amplitude and phase for each interferometric element. We define the requirements for such a source, describe an initial implementation and verification of this source using commercial Software Defined Radio boards, and present beam map slices from antenna range measurements using the commercial boards. We found that the commercial board did not meet all requirements, so we also suggest future directions using a more sophisticated chipset.
The Who in Code-Switching: A Case Study for Predicting Egyptian Arabic–English Code-Switching Levels Based on Character Profiles
Authors: Hamed Injy , El Bolock Alia , Herbert Cornelia , Abdennadher Slim , Vu Ngoc Thang
Published: August 2022

Code-switching (CS) is a common linguistic phenomenon exhibited by multilingual individuals, where they tend to alternate between languages within one single conversation. CS is a complex phenomenon that not only encompasses linguistic challenges, but also contains a great deal of complexity in terms of its dynamic behavior across speakers. Given that the factors giving rise to CS vary from one country to another, as well as from one person to another, CS is found to be a speaker-dependent behavior, where the frequency by which the foreign language is embedded differs across speakers. While several researchers have looked into predicting CS behavior from a linguistic point of view, research is still lacking in the task of predicting the user CS behavior from sociological and psychological perspectives. We provide an empirical user study, where we investigate the correlations between users’ CS levels and character traits. We conduct interviews with bilinguals and gather information on their profiles, including their demographics, personality traits, and traveling experiences. We then use machine learning (ML) to predict users’ CS levels based on their profiles, where we identify the main influential factors in the modeling process. We experiment with both classification as well as regression tasks. Our results show that the CS behavior is affected by the relation between speakers, travel experiences, as well as Neuroticism and Extraversion personality traits.
The Ammonites of the Green Ammonite Beds of Dorset
Authors: Spath Leonard Frank
Published: August 2022

I. Introduction.

As on previous occasions when I have had the privilege of examining the ammonites of the Lower Lias of Charmouth, collected by Dr. W. D. Lang, I propose to discuss the identifications, given in his stratigraphical account; also to comment on the generic classification and the phylogenetic relationships of the ammonites. Of four families recorded, only one (the Liparoceratidæ) is really abundantly represented and important; but on account of the necessity for adequate illustration I am publishing a full discussion of the phylogeny of this family elsewhere. Here I may stress the fact that the V-shaped peripheral ribs so characteristic of the late capricorns first appear on the inner (not the outer) whorls of the early forms, a clear case of cænogenesis, as palæontologists understand it, and comparable to that of schlotheimids (Spath, 1924, p. 198); also, the fact that the restriction of the various ammonites to certain beds is fortuitous and peculiar to Dorset, having nothing to do with the real range of these fossils. That is to say, the discovery of an
Amaltheus
at a level 11 feet below the Lowest Tier enables us to fix the local base of the Margaritatus Zone, and it is convenient that, at least up to the present,
Oistoceras
has not been found to range higher than within three feet of the lowest
Amaltheus
. But where
Oistoceras
and
Amaltheus
overlap, or where even
Prodactylioceras davœi
passes up into the Margaritatus Zone, or where
Liparoceras
(
Becheiceras
) has its maximum in the
A Relational Program Logic with Data Abstraction and Dynamic Framing
Authors: Banerjee Anindya , Nagasamudram Ramana , Naumann David A. , Nikouei Mohammad
Published: August 2022

Dedicated to Tony Hoare.

In a paper published in 1972, Hoare articulated the fundamental notions of hiding invariants and simulations. Hiding: invariants on encapsulated data representations need not be mentioned in specifications that comprise the API of a module. Simulation: correctness of a new data representation and implementation can be established by proving simulation between the old and new implementations using a coupling relation defined on the encapsulated state. These results were formalized semantically and for a simple model of state, though the paper claimed this could be extended to encompass dynamically allocated objects. In recent years, progress has been made toward formalizing the claim, for simulation, though mainly in semantic developments. In this article, hiding and simulation are combined with the idea in Hoare’s 1969 paper: a logic of programs. For an object-based language with dynamic allocation, we introduce a relational Hoare logic with stateful frame conditions that formalizes encapsulation, hiding of invariants, and couplings that relate two implementations. Relations and other assertions are expressed in first-order logic. Specifications can express a wide range of relational properties such as conditional equivalence and noninterference with declassification. The proof rules facilitate relational reasoning by means of convenient alignments and are shown sound with respect to a conventional operational semantics. A derived proof rule for equivalence of linked programs directly embodies representation independence. Applicability to representative examples is demonstrated using an SMT-based implementation.
Inward rectifier potassium (Kir) channels in the retina: living our vision
Authors: Beverley Katie M. , Pattnaik Bikash R.
Published: August 2022

Channel proteins are vital for conducting ions throughout the body and are especially relevant to retina physiology. Inward rectifier potassium (Kir) channels are a class of K+ channels responsible for maintaining membrane potential and extracellular K+ concentrations. Studies of the KCNJ gene (that encodes Kir protein) expression identified the presence of all of the subclasses (Kir 1–7) of Kir channels in the retina or retinal-pigmented epithelium (RPE). However, functional studies have established the involvement of the Kir4.1 homotetramer and Kir4.1/5.1 heterotetramer in Müller glial cells, Kir2.1 in bipolar cells, and Kir7.1 in the RPE cell physiology. Here, we propose the potential roles of Kir channels in the retina based on the physiological contributions to the brain, pancreatic, and cardiac tissue functions. There are several open questions regarding the expressed KCNJ genes in the retina and RPE. For example, why does not the Kir channel subtype gene expression correspond with protein expression? Catching up with multiomics or functional “omics” approaches might shed light on posttranscriptional changes that might influence Kir subunit mRNA translation within the retina that guides our vision.
Convergence of signaling pathways in mediating actions of leucine and IGF-1 on mTORC1 in L6 myoblasts
Authors: Roberson Paul A. , Jefferson Leonard S. , Kimball Scot R.
Published: August 2022

Leucine and insulin-like growth factor-1 (IGF-1) are important regulators of protein synthesis in skeletal muscle. The mechanistic target of rapamycin complex 1 (mTORC1) is of particular importance in their mechanism of action. In the present study, pathways through which leucine and IGF-1 converge to mediate activation of mTORC1 were examined in L6 myoblasts that were deprived of leucine and serum followed by readdition of either leucine or IGF-1. Compared with leucine- and serum-deprived myoblasts, IGF-1, but not leucine, promoted phosphorylation of protein kinase B (AKT), tuberous sclerosis complex 2 (TSC2), and the autophosphorylation site on mTOR (S2481) and also stimulated mTOR kinase activity in mTOR immunoprecipitated samples. Both leucine and IGF-1 promoted phosphorylation of mTOR on S2448. The association of mTOR with the regulatory-associated protein of mTOR (Raptor) was altered by IGF-1 treatment and trended ( P = 0.065) to be altered by leucine treatment. Alterations in the association of mTOR with Raptor were proportional to changes in phosphorylation of the mTOR substrates, eIF4E-binding protein 1 (4E-BP1), and ribosomal protein S6 Kinase-β1 (p70S6K1). Surprisingly, leucine, but not IGF-1, stimulated protein synthesis suggesting a unique role for nutrients in regulating protein synthesis. Overall, the results are consistent with a model whereby IGF-1 stimulates phosphorylation of 4E-BP1 and p70S6K1 in L6 myoblasts through an AKT-TSC2-mTORC1 signaling pathway that also involves changes in the interaction between mTOR and Raptor. In contrast, leucine signaling to mTOR results in alterations in certain mTOR phosphorylation sites and the interaction between mTOR and Raptor and stimulates protein synthesis.
The sugar daddy: the role of the renal proximal tubule in glucose homeostasis
Authors: Hotait Zahraa S. , Lo Cascio Julia N. , Choos Elijah N. D. , Shepard Blythe D.
Published: August 2022

Renal blood flow represents >20% of total cardiac output and with this comes the great responsibility of maintaining homeostasis through the intricate regulation of solute handling. Through the processes of filtration, reabsorption, and secretion, the kidneys ensure that solutes and other small molecules are either returned to circulation, catabolized within renal epithelial cells, or excreted through the process of urination. Although this occurs throughout the renal nephron, one segment is tasked with the bulk of solute reabsorption—the proximal tubule. Among others, the renal proximal tubule is entirely responsible for the reabsorption of glucose, a critical source of energy that fuels the body. In addition, it is the only other site of gluconeogenesis outside of the liver. When these processes go awry, pathophysiological conditions such as diabetes and acidosis result. In this review, we highlight the recent advances made in understanding these processes that occur within the renal proximal tubule. We focus on the physiological mechanisms at play regarding glucose reabsorption and glucose metabolism, emphasize the conditions that occur under diseased states, and explore the emerging class of therapeutics that are responsible for restoring homeostasis.
Substrate stiffness modulates migration and local intercellular membrane motion in pulmonary endothelial cell monolayers
Authors: Paudel Sunita Subedi , deWeever Althea , Sayner Sarah , Stevens Troy , Tambe Dhananjay T.
Published: August 2022

The pulmonary artery endothelium forms a semipermeable barrier that limits macromolecular flux through intercellular junctions. This barrier is maintained by an intrinsic forward protrusion of the interacting membranes between adjacent cells. However, the dynamic interactions of these membranes have been incompletely quantified. Here, we present a novel technique to quantify the motion of the peripheral membrane of the cells, called paracellular morphological fluctuations (PMFs), and to assess the impact of substrate stiffness on PMFs. Substrate stiffness impacted large-length scale morphological changes such as cell size and motion. Cell size was larger on stiffer substrates, whereas the speed of cell movement was decreased on hydrogels with stiffness either larger or smaller than 1.25 kPa, consistent with cells approaching a jammed state. Pulmonary artery endothelial cells moved fastest on 1.25 kPa hydrogel, a stiffness consistent with a healthy pulmonary artery. Unlike these large-length scale morphological changes, the baseline of PMFs was largely insensitive to the substrate stiffness on which the cells were cultured. Activation of store-operated calcium channels using thapsigargin treatment triggered a transient increase in PMFs beyond the control treatment. However, in hypocalcemic conditions, such an increase in PMFs was absent on 1.25 kPa hydrogel but was present on 30 kPa hydrogel—a stiffness consistent with that of a hypertensive pulmonary artery. These findings indicate that 1) PMFs occur in cultured endothelial cell clusters, irrespective of the substrate stiffness; 2) PMFs increase in response to calcium influx through store-operated calcium entry channels; and 3) stiffer substrate promotes PMFs through a mechanism that does not require calcium influx.
Endosomal parathyroid hormone receptor signaling
Authors: Peña Karina A.
Published: August 2022

The canonical model for G protein-coupled receptors (GPCRs) activation assumes that stimulation of heterotrimeric G protein signaling upon ligand binding occurs solely at the cell surface and that duration of the stimulation is transient to prevent overstimulation. In this model, GPCR signaling is turned-off by receptor phosphorylation via GPCR kinases (GRKs) and subsequent recruitment of β-arrestins, resulting in receptor internalization into endosomes. Internalized receptors can then recycle back to the cell surface or be trafficked to lysosomes for degradation. However, over the last decade, this model has been extended by discovering that some internalized GPCRs continue to signal via G proteins from endosomes. This is the case for the parathyroid hormone (PTH) type 1 receptor (PTHR), which engages on sustained cAMP signaling from endosomes upon PTH stimulation. Accumulative evidence shows that the location of signaling has an impact on the physiological effects of GPCR signaling. This mini-review discusses recent insights into the mechanisms of PTHR endosomal signaling and its physiological impact.
Comparison and Economic Envelope Structure Schemes for Deep Foundation Pit of Subway Stations Based on Fuzzy Logic
Authors: An Puzhen , Liu Ziming , Jia Baoxin , Zhou Quan , Meng Fanli , Wang Zhixin
Published: August 2022

Since the 1990s, with the continuous advancement of urbanization, the proportion of urban population has gradually increased. There is a serious shortage of land resources, and people’s demand for underground space is increasing day by day. The construction of subway stations has developed into an inevitable trend in the future construction engineering industry, and it is also necessary to select the best solution from various solutions. The purpose of this paper is to study how to evaluate and analyze the economy of the deep foundation pit envelope structure of subway stations based on fuzzy logic, so as to choose the optimal and most economical plan. This paper proposes a fuzzy comprehensive evaluation method based on fuzzy logic, which is a reasonable method for the classic evaluation index. The experimental results of this paper show that in 2015 about 8% of people chose to travel by subway. By 2020, 54.5% of people chose to travel by subway, an increase of 46.5% during this period. It can be seen that more and more people are willing to take the subway, and subway transportation is a public transportation mode with large transportation volume. It has obvious public welfare, and it can relieve the urban traffic pressure very well, so the investment in subway construction in various cities is also increasing.
Using Convolutional Neural Network as a Statistical Algorithm to Explore the Therapeutic Effect of Insulin Liposomes on Corneal Inflammation
Authors: Liao Yukun , Jiang Huiting , Du Yangrui , Xiong Xiaojing , Zhang Yu , Du Zhiyu
Published: August 2022

Aiming at the disadvantages of easy recurrence of keratitis, difficult eradication by surgery, and easy bacterial resistance, insulin-loaded liposomes were prepared, and convolutional neural network was used as a statistical algorithm to build SD rat corneal inflammation model and study insulin-loaded liposomes, alleviating effect on corneal inflammatory structure in SD rats. The INS/PFOB@LIP was developed by means of thin-film dispersive phacoemulsification, its structure was monitored using a transmission electron microscope, particle size and appearance potential were monitored using a Malvern particle sizer, and ultraviolet consumption spectrum was monitored using a UV spectrophotometer. The encapsulation rate, drug loading, and distribution of insulin liposomes in rat corneal inflammatory model were measured and calculated. The cytotoxicity of liposome materials was evaluated by CCK-8 assay, and the toxic effects of insulin and insulin liposomes on cells were detected. The cornea of SD rats was burned with NaOH solution (1 mol/L), and the SD rat corneal inflammation model was created. The insulin liposome was applied to the corneal inflammation model, and the therapeutic effect of insulin liposome on corneal inflammation was evaluated by slit lamp, corneal immunohistochemistry, corneal HE staining, and corneal Sirius red staining. Insulin-loaded liposomes were successfully constructed with an average particle size of (130.69 ± 3.87) nm and a surface potential of (−38.24 ± 2.57) mV. The encapsulation rate of insulin liposomes was (48.89 ± 1.24)%, and the drug loading rate was (24.45 ± 1.24)%. The SD rat corneal inflammation model was successfully established. After insulin liposome treatment, the staining area of corneal fluorescein sodium was significantly reduced, the corneal epithelium was significantly thickened, the content of corneal collagen was increased, the expression of inflammatory factors was significantly reduced, and new blood vessels (corneal neovascularization, CNV) growth was inhibited.
The Fusion Model of Financial Accounting and Management Accounting Based on Neural Networks
Authors: Chen Xiaowei
Published: August 2022

The lines between management accounting and financial accounting are constantly blending as a result of the new economic norm, which is driving this relationship closer and closer. To increase the working effectiveness of financial departments, the two must be coordinated and integrated. The efficient fusion of FA and MA can significantly impact not only the actual effectiveness of accounting work but also the long-term success of businesses. This study develops a network structure that combines BPNN (BP neural network) and PLS (partial least squares), and it applies the structure to FA and MA. The financial data that has been gathered is preprocessed using PLS, which keeps the primary factors of the data processing outcomes and ignores the secondary factors. The findings indicate that this model has a 90 percent overall accuracy rate when assessing the financial health of the 50 test sample companies. The analysis in the conclusion demonstrates that this model can successfully examine the internal relationship between a company’s capital structure and financial performance. It also serves as a guide for the company in terms of how to improve its current illogical financing structure system and improve financial performance.
Value of Artificial Neural Network Ultrasound in Improving Breast Cancer Diagnosis
Authors: Chai Qiaolian , Mei Lixue , Zou Zhenxing , Peng Haixia
Published: August 2022

Ultrasound-guided needle biopsy based on artificial neural network, as a safe, effective, and simple preoperative pathological diagnosis technique, has been widely used in clinical practice. Ultrasound-guided needle biopsy based on artificial neural networks for suspicious breast lesions found in conventional ultrasound examinations is an effective method for preoperative diagnosis. The purpose of this article is to study the value of artificial neural network ultrasound in improving breast cancer diagnosis. This article summarizes the neuron model of PCNN by observing and studying its impulse synchronization phenomenon. Aiming at gray-scale images disturbed by mixed noise (impulse noise and the Gaussian noise), a comprehensive filtering algorithm based on the simplified PCNN model is proposed. In this paper, the benign and malignant breast masses were evaluated based on the two-dimensional and three-dimensional ultrasound imaging signs of the mass, and compared with the postoperative pathological results, a logistic regression model was established to analyze the shape, boundary, microcalcification, and posterior echo attenuation of the mass, values for keratinization or burrs, convergent signs, and blood flow classification in the differential diagnosis of benign and malignant. In this paper, a color ultrasound diagnostic device is used, Sonobi is used as a contrast medium, and the injection volume is 2.4 ml/dose. During the imaging process, the sound image performance of the lesion is dynamically observed, the original dynamic data are stored throughout the whole process, and the playback analysis is performed after the imaging is completed. Studies have shown that CDUS elastography (UE) combined with MRI can increase the sensitivity of breast cancer diagnosis, with a diagnostic accuracy rate of 92.4%.
CR-BA: Public Key Infrastructure Certificate Revocation Scheme Based on Blockchain and Accumulator
Authors: Xie Jingxue , Tan Xinghong , Tan Liang
Published: August 2022

With the development of blockchain, many studies apply blockchain to certificate revocation. However, existing blockchain-based certificate revocation schemes have two shortcomings. First, the storage overhead on the blockchain is relatively large. Second, as the number of revoked certificates increases, the misjudgment rate of certificate status will increase accordingly, so a public key infrastructure implementation certificate revocation scheme based on blockchain and accumulators, called CR-BA, is proposed. First, CR-BA expands the certificate structure, adding a revocation factor and a smart contract account for accessing the blockchain in the certificate extension, which is filled by the CA when the certificate is generated. Then, when the certificate is to be revoked, CA generates the revocation fingerprint through the revocation factor and publishes it to the blockchain. Finally, when the user needs to verify the status of the certificate, CA calculates the revocation fingerprint according to the revocation factor on the certificate, then compares it with the existing revocation fingerprint on the blockchain, and returns the comparison result to the user. The experimental results show that this scheme can effectively overcome the storage and misjudgment problems caused by existing blockchain-based certificate revocation schemes and improve the query efficiency of certificate revocation information.
Research on Optimization Scheme for Blocking Artifacts after Patch-Based Medical Image Reconstruction
Authors: Xu Yan , Hu Shunbo , Du Yuyue
Published: August 2022

Due to limitations of computer resources, when utilizing a neural network to process an image with a high resolution, the typical processing approach is to slice the original image. However, because of the influence of zero-padding in the edge component during the convolution process, the central part of the patch often has more accurate feature information than the edge part, resulting in image blocking artifacts after patch stitching. We studied this problem in this paper and proposed a fusion method that assigns a weight to each pixel in a patch using a truncated Gaussian function as the weighting function. In this method, we used the weighting function to transform the Euclidean-distance between a point in the overlapping part and the central point of the patch where the point was located into a weight coefficient. With increasing distance, the value of the weight coefficient decreased. Finally, the reconstructed image was obtained by weighting. We employed the bias correction model to evaluate our method on the simulated database BrainWeb and the real dataset HCP (Human Connectome Project). The results show that the proposed method is capable of effectively removing blocking artifacts and obtaining a smoother bias field. To verify the effectiveness of our algorithm, we employed a denoising model to test it on the IXI-Guys human dataset. Qualitative and quantitative evaluations of both models show that the fusion method proposed in this paper can effectively remove blocking artifacts and demonstrates superior performance compared to five commonly available and state-of-the-art fusion methods.
Application of Artificial Intelligence Algorithm and VR Technology in Vocal Music Teaching
Authors: Jing Liu
Published: August 2022

Traditional intelligent algorithms mainly artificially preselect the geometric features of the image and then classify and recognize the image based on these features. The geometric features of the manually selected working images are often interfered by human factors, resulting in inaccurate feature extraction and reduced classification accuracy. Aiming at the above problems, an intelligent recognition method based on artificial intelligence algorithm is proposed. We will deeply analyze the application of the core technology of the system and introduce the virtual education function of the application through relevant content. The application of this technology can effectively improve the efficiency of vocal music teaching and enable students to obtain a richer educational experience. Then, this paper finishes the other parts of the system again, realizes the functional modules of the system one by one, and elaborates the corresponding interface diagrams. Finally, a suitable testing method can be used to test the performance modules and performance conditions of the system to ensure that the system has good reliability and availability and put it into production. The voice teaching system based on virtual reality technology is more advanced and superior than other teaching systems on the market today and can provide users with a more realistic experience. In addition, users can have more choices in the process of virtual education, thereby improving the efficiency and quality of education.
Machine Learning-Driven Enterprise Human Resource Management Optimization and Its Application
Authors: Sun Jingtong
Published: August 2022

With the advent of the Internet era, the frequency and proportion of candidates obtaining recruitment information through the Internet is getting higher and higher, and the amount of human resource information, such as talent information and job information, has also increased unprecedentedly, which makes human resource services face information overload. Especially with the gradual increase of the amount of information, this method is not enough for the acquisition and classification of massive data. After that, experts developed search engines to deal with the retrieval problem, and the first ones were Google and Baidu. As long as the search engine is clear about the direction of the search, it is indeed very convenient for the retrieval of massive data. However, in many cases, most users cannot clearly recognize the content they need or how to accurately express their needs. Faced with this problem, people propose recommender systems to solve the problem of obtaining preference information, which can better increase the user’s experience and meet their own needs more easily. Based on the main workflow of the recommender system, this paper designs the overall architecture of the human resources recommendation system and implements a human resources recommendation prototype system based on deep learning. The system can better overcome the cold start problem and provide real-time recommendation results, improving the quality of HR personalized recommendation results.
Mitigating GNSS Multipath Effects Using XGBoost Integrated Classifier Based on Consistency Checks
Authors: Li Dengao , Ma Xiaoli , Zhao Jumin , Wu Fanming
Published: August 2022

Under the influence of urban building roads, especially interference from multipath effects, global navigation satellite system (GNSS) receiver-related output signal distortion can affect the robustness of the positioning system and the final positioning accuracy. To deal with the above problems, this paper proposes a two-layer consistency-checks (CC) positioning model based on eXtreme Gradient Boosting (XGBoost) integrated learner. First, the model excludes the abnormal values from the correlated output of the first layer by the classical statistical distribution test method. Then, the remaining available measurements are used as the second-layer input, and the measurements are used as learning data using an integrated machine learning method, XGBoost, to efficiently detect and identify non-line-of-sight (NLOS), LOS, and other reflective multipath signals. In order to better mitigate errors in the dynamic relative positioning process, the second-layer checking process uses dynamic pseudorange differencing technique (DPDT) and weighted least squares method (WLS) to smooth the output outcome of the receiver. In the experimental part, we compare and analyze the proposed method with the existing methods from different perspectives in this paper, respectively. The results show that the performance of the model is significantly improved after applying the CC method, in which the average classification accuracy of the multipath signals in the target feature set can reach 91.6%. According to the final positioning results, the proposed method shows a significant accuracy improvement compared to the existing research methods.
N1-Methyladenosine-Related lncRNAs Are Potential Biomarkers for Predicting Prognosis and Immune Response in Uterine Corpus Endometrial Carcinoma
Authors: Liu Jinhui , Geng Rui , Zhong Zihang , Zhang Yixin , Ni Senmiao , Liu Wen , Du Mulong , Bai Jianling
Published: August 2022

Uterine corpus endometrial carcinoma (UCEC) is a malignant disease that, at present, has no well-characterised prognostic biomarker. In this study, two clusters were identified based on 28 N1-methyladenosine- (m1A-) related long noncoding RNAs (lncRNAs), of which cluster 1 was related to immune pathways according to the results of an enrichment analysis. We further observed better prognosis in patients with higher levels of immune cell infiltration, tumor mutation burden, microsatellite instability, and immune checkpoint gene expression. In addition, through Cox regression analysis and least absolute shrinkage and selection operator regression analysis, 10 m1A-related lncRNAs (mRLs) were employed to build a prognosis model. We found that people in higher risk categories had a poorer survival probability than those in lower risk. Low-risk samples were enriched with immune-related pathways, while the high-risk group was similar to the definition of the “immune desert” phenotype, which was associated with decreased immune infiltration, T cell failure, and decreased tumor mutation burden, while also being insensitive to immunotherapy and chemotherapy. This mRL-based model has the ability to accurately predict the prognosis of UCEC patients, and the mRLs could become promising therapeutic targets in enhancing the response of immunotherapy.
Effects of Age-Appropriate Mobile APP Use Behavior on Subjective Well-Being of Young Elderly
Authors: Liu Wei , Tang Weihong
Published: August 2022

The structural equation model was used to empirically analyze the influence of the use of age-appropriate APP on the subjective well-being of the younger elderly. The results show that the attitudes of the younger elderly towards the use of age-appropriate apps are significantly affected by perceived usefulness and perceived ease of use, and perceived usefulness has a significant effect on perceived ease of use. The willingness of the younger elderly to use apps for aging is significantly affected by behavioral attitude, subjective norms, and perceived behavioral control. The behavior of using age-appropriate APP has a significant impact on the subjective well-being of the young elderly. The use of age-appropriate apps by the younger elderly is a positive and selective behavior, which is conducive to the younger elderly to actively adapt to and accept new technologies and improve the life satisfaction of the younger elderly.
Application of RQMC for CDO Pricing with Stochastic Correlations under Nonhomogeneous Assumptions
Authors: Qu Shuanghong , Meng Lingxian , Li Hua
Published: August 2022

In consideration of that the correlation between any two assets of the asset pool is always stochastic in the actual market and that collateralized debt obligation (CDO) pricing models under nonhomogeneous assumptions have no semianalytic solutions, we designed a numerical algorithm based on randomized quasi‐Monte Carlo (RQMC) simulation method for CDO pricing with stochastic correlations under nonhomogeneous assumptions and took Gaussian factor copula model as an example to conduct experiments. The simulation results of RQMC and Monte Carlo (MC) method were compared from the perspective of variance changes. The results showed that this numerical algorithm was feasible, efficient, and stable for CDO pricing with stochastic correlation under nonhomogeneous assumptions. This numerical algorithm is expected to be extended to other factor Copula models for CDO pricing with stochastic correlations under nonhomogeneous assumptions.
Algorithm of Apriori-Based Rural Tourism Driving Factors and Its System Optimization
Authors: Xiao Hongyi
Published: August 2022

Rural tourism, as a vital component of tourism, is critical to the development of rural economies, farmers’ income, rural civilization, new rural construction, and urban-rural interaction. Simultaneously, as the size and complexity of data sets grow larger, how to improve the efficiency of association rule algorithms for mining large data sets has become a hot topic in association rule mining. Rural tourism development that is cultural and creative not only contributes to rural revitalization, but also to the preservation and inheritance of rural culture. The Apriori algorithm is the most widely used and influential algorithm for mining Boolean association rules, and the majority of current algorithms are extensions of the Apriori algorithm. Demand, supply, marketing, and support forces of rural tourism, which are the core driving force of rural tourism development, are formed by the basic needs of each subsystem of rural tourism. One of the main methods is to promote the sustainable and healthy development of rural tourism in accordance with the nature, characteristics, and laws of rural tourism destination construction, in order to create a dynamic system for long-term development and establish a rural tourism development dynamic system. The study of rural tourism driving factors and their system optimization is proposed in this paper. The main tourism dynamic system is adopted by the association rule algorithm of Apriori, the driving factors of rural tourism development are analyzed in the paper, and the system optimization method is proposed, all based on the Apriori algorithm. In terms of support, the Apriori algorithm is 0.436 higher than the CD algorithm and 0.568 higher than the SVM algorithm, and the Apriori algorithm can greatly reduce database size and improve record reading speed. As a result, the findings of this paper can be used to improve the spatial layout of rural tourism and to develop urban-rural tourism.
Study of the Antimicrobial Activity of the Chinese Dong Ethnic Minority Medicine, Madeng’ai
Authors: Tang Zhenrong , Zhao Yannan , Zhang Zaiqi , Yue Huan , Wang Dan , Liu Shengchun , Tang Hua
Published: August 2022

The overuse of antibiotics has contributed to the emergence of multidrug-resistant bacteria, which poses a challenging task for clinical therapy. Thus, new agents with antibiotic efficacy against multidrug-resistant infections are needed. The traditional Dong ethnic minority medicines have emerged as a new source for prodrug selection. Among them, Madeng’ai (PotentillafreynianaBornm) is widely used by the folk for anti-infection and wound healing, although the mechanisms remain unclear. In this study, the antimicrobial activities of Dong medicine Madeng’ai were evaluated both in vitro and in vivo. S. aureus, E. coli, E. faecalis, P. aeruginosa, K. pneumoniae, and A. baumannii were cultured in LB media, different concentrations of Madeng’ai powder solution were added to the LB agar plates to evaluate minimal inhibitory concentration. An animal study was performed on a mouse excisional wound model combined with bacterial solution injection in the wound area. After Madeng’ai or PBS treatment, hematoxylin and eosin analysis were used for pathological analysis of skin tissues from the infected area. Madeng’ai powder solution over 2 mg/mL concentration completely inhibited E. coli growth. At 4.0 mg/mL, Madeng’ai significantly inhibited the growth of E. faecalis, Pseudomonas aeruginosa (PAE), Klebsiella pneumoniae, and Acinetobacter baumannii. The mouse model revealed that Madeng’ai could suppress the growth of MRSA and PAE and accelerate healing of cutaneous wounds. Madeng’ai, a newly discovered Dong ethnic minority medicine possesses considerable antimicrobial activity against both human normal pathogenic bacteria and multiresistance bacteria such as Pseudomonas aeruginosa, S. aureus, and Acinetobacter baumannii. Therefore, Madeng’ai has great potential for further study and clinical application.
Data Analysis of Educational Evaluation Using K-Means Clustering Method
Authors: Liu Rui
Published: August 2022

It is thought to be an effective technique to handle the problem of educational data explosion and lack of information by identifying potential relationships between data and directing decision-makers through the extraction, transformation, analysis, and modeling of educational data. Based on this, this research constructs a data analysis model for education evaluation using the K-means clustering technique in DM. The weight of each index of students’ comprehensive quality is calculated using AHP, and the value of the weight is used to determine whether the index is the important feature of analysis system mining. Improved sampling technology is used to deal with the representation of large-scale data sets; a sample partition clustering technique is proposed as a general framework. The best accuracy of this method, according to experimental data, is 95.6 percent, which is 12.1 percent greater than Mi cluster algorithm and 6.8 percent higher than DRCluster algorithm. The K-means clustering analysis technology is used to analyze students’ comprehensive evaluation data in this paper, with the goal of determining the regularity of data implication, accurately diagnosing learning problems, and providing the foundation for developing effective student management strategies.
Superficial Retinal Microvasculature and Choriocapillaris Alterations after Photodynamic Therapy in Chronic Central Serous Chorioretinopathy
Authors: Entezari Morteza , Ansari Iman , Ramezani Alireza , Nikkhah Humayoun , Mohammadi Parisa , Kheiri Bahar
Published: August 2022

Purpose. To compare superficial retinal vascular, choriocapillaris (CC), and choroidal thickness changes in chronic central serous chorioretinopathy (CSCR) patients after half-dose photodynamic therapy (PDT). Method. In this prospective interventional case series study, fifteen eyes of 14 patients with chronic CSCR undergoing half-dose PDT treatment were enrolled. All patients underwent enhanced depth imaging optical coherence tomography angiography (EDI-OCT) and optical coherence tomography angiography (OCTA) at baseline and 3 months after treatment. Best-corrected visual acuity (BCVA), superficial retinal vascular density, CC vessel density, foveal avascular zone (FAZ), central macular thickness (CMT), and choroidal thickness were compared. Results. Mean BCVA before and after PDT was 0.34 ± 0.26 and 0.19 ± 0.25 logMAR, respectively (

p
=
0.011

). Mean FAZ before treatment was 410.21 ± 117.00 μm2, which increased to 433.50 ± 116.76 μm2 (

P
=
0.253

). Mean vessel density at superficial capillary plexus (SCP) was 38.93 ± 11.12 at baseline, which increased to 39.04 ± 11.43 (

P
=
0.886

). Mean CC vessel density was 53.21 ± 4.14 at baseline, which significantly decreased to 51.85 ± 4.21 (

P
=
<
0.001

). BCVA has no significant correlation with FAZ (

P
=
0.282

) and vessel density (

P
=
0.0.241

) at SCP. CMT significantly decreased from 380.87 ± 41.66 μ at baseline to 268.20 ± 28.07 μ at 3 months (

P
=
0.132

). We did not find any correlation between CMT and FAZ (

P
=
0.040

) and vessel density (

P
=
0.686

) at SCP. Mean subfoveal choroidal thickness reduced from 411 ± 171 μm before treatment to 372 ± 117 μm (

P
=
0.106

). Conclusion. PDT treatment can affect retinal and choroidal structural parameters, but we could not find any significant influence on retinal vascular parameters, including FAZ area and vessel densityonly mean CC vessel density and subfoveal choroidal thickness decreased.
The Value of 18F‐FDG PET/CT in the Diagnosis of Tuberculous Pleurisy and in the Differential Diagnosis between Tuberculous Pleurisy and Pleural Metastasis from Lung Adenocarcinoma
Authors: Du Xiaoqing , Zhu Feng , Yu Chunjing
Published: August 2022

Objectives. This study aims to investigate the diagnostic value of 18F‐FDG PET/CT in tuberculous pleurisy (TBP) and the differential diagnostic value of 18F‐FDG PET/CT between TBP and pleural metastasis from lung adenocarcinoma (PMLAC). Materials and Methods. The features of pleura on PET and hybrid CT were retrospectively studied in 20 patients with TBP and 32 patients with PMLAC. The ROC curve was used to evaluate the diagnostic effectiveness of these indices for TBP and PMLAC, and binary logistic regression analysis was conducted to identify independent predictors of TBP and PMLAC. Results. There were significant differences in pleural 18F‐FDG uptake pattern on PET (P = 0.001), pleural morphology pattern on CT (P = 0.002), the maximum diameter of the pleural nodule (P = 0.001), and interlobular fissure nodule (P = 0.001) between TBP and PMLAC groups. The diffused pleural FDG uptake type on PET (odds ratio (OR) = 6.0, 95% CI 2.216–16.248, P = 0.001) and the lamellar pleural thickening type on CT (OR = 4.4, 95% CI 2.536–7.635, P = 0.001) were independent predictors of TBP, with 60% and 55% sensitivity, 96.6% and 90.6% specificity, and 82.7% and 77.0% accuracy. The combined diagnostic sensitivity, specificity, and accuracy for TBP were 70%, 87.5%, and 80.8%. The mixed pleural FDG uptake type on PET (OR = 5.106, 95% CI 2.024–12.879, P = 0.001), the mixed pleural thickening type on CT (OR = 2.289, 95% CI 1.442–3.634, P = 0.001), and the maximum diameter of the pleural nodule (OR = 1.027, 95% CI 1.012–1.042, P = 0.001) were independent predictors of PMLAC, with 78.1%, 71.9%, and 87.5% sensitivity, 85%, 80%, and 85% specificity, and 80.8%, 75%, and 86.5% accuracy. The combined diagnostic sensitivity, specificity, and accuracy for PMLAC were 96.9%, 85%, and 90.4%. Conclusions. 18F‐FDG PET/CT is of great clinical value in the diagnosis of TBP and in the differential diagnosis between TBP and PMLAC.
Changes in the Surface Water Nitrogen Content in the Upper Hun River Basin, Northeast China
Authors: Jin Wenkai , Ma Jian , Chen Xin , Yan Guohui
Published: August 2022

Human activities have considerably increased nitrogen intake into waterways, resulting in the deterioration of water quality. The state of surface water requires special consideration in light of the water crisis caused by nitrogen pollution. In this study, the natural abundance of the nitrogen stable isotope (δ15N) is measured and sampled in sediments and compared with the total dissolved nitrogen (DN) in four main Chinese tributaries of Hun River upper reach, including the Dasuhe, Beisanjia, Beikouqian, and Nanzamu tributaries. Results show that for the Dasuhe and Nankouqian tributaries, the δ15N values of sediment samples in 2016 are all significantly higher than previous values in 2011. In the Dasuhe tributary, this change is attributed to the promotion of organic agricultural production under which chemical fertilizers are replaced by organic fertilizers. For the δ15N values of the sediment in the Nankouqian tributary, the construction of the municipal sewer system and wastewater treatment facilities are the causes of this rising trend. The δ15N values of nitrate released by facilities could be raised by microbial denitrification that is employed in the tertiary treatment process. Most of the δ15N values of the sediments are distributed between soil and manure, indicating that nitrogen in the river water mainly comes from agriculture. All the surveyed tributaries except Dasuhe show a significant increase in DN. In addition, a significant positive correlation between the change ratio of the farmland area and DN in river water is observed, suggesting that the increase in nitrogen in river water from 2011 to 2016 is due to agriculture. Based on the abovementioned data, this study provides a basis for local governments to formulate management measures.
An Extremely Effective Spatial Pyramid and Pixel Shuffle Upsampling Decoder for Multiscale Monocular Depth Estimation
Authors: Luo Huilan , Chen Yuan , Zhou Yifeng
Published: August 2022

To estimate the accurate depth from a single image, we proposed a novel and effective depth estimation architecture to solve the problem of missing and blurred contours of small objects in the depth map. The architecture consists of Extremely Effective Spatial Pyramid modules (EESP) and Pixel Shuffle upsampling Decoders (PSD). The results of this study show that multilevel information and the upsampling method in the decoders are essential for recovering the accurate depth map. Through the model we proposed, competitive performance compared with state-of-the-art methods in terms of reconstruction of object boundaries and the detection rate of small objects has been demonstrated. Our approach has wide applications in higher-level visual tasks, including 3D reconstruction and autonomous driving.