bluuebunny/crossref_metadata_2025 huggingface dataset:
Broadband Acoustic Metamaterial Design via Machine Learning
Authors: Tran Thang , Amirkulova Feruza A. , Khatami Ehsan
Published: August 2022

Acoustic metamaterials are engineered microstructures with special mechanical and acoustic properties enabling exotic effects such as wave steering, focusing and cloaking. In this research, we develop a new machine learning framework for predicting optimal metastructures such as planar configurations of scatterers with specific functionalities. Specifically, we implement this framework by combining probabilistic generative modeling with deep learning and propose two models: a conditional variational autoencoder (CVAE) and a supervised variational autoencoder (SVAE) model. As an application of the method, here we design an acoustic cloak considering a minimization of total scattering cross-section (TSCS) for a set of cylindrical obstacles. We work with the sets of cylindrical objects confined in a region of space and streamline the design of configurations with minimal TSCS, demonstrating broadband cloaking effect at discrete sets of wavenumbers. After establishing the artificial neural networks that are capable of learning the TSCS based on the location of cylinders, we discuss our inverse design algorithms, combining variational autoencoders and the Gaussian process, for predicting optimal arrangements of scatterers given the TSCS. We show results for up to eight cylinders and discuss the efficiency and other advantages of the machine learning approach.
Estimation of rock mass deformability based on empirical relations for Ghezel Ozan dam site in the north of Iran
Authors: Motamed-Shariati Ehsan , Motevalizadeh Mehdi , Teshnizi Ebrahim Sharifi
Published: August 2022

Rock mass deformability modulus is one of the most important parameters for underground structures and dam site design. The current methods for determining this parameter are
in situ
tests and empirical equations.
In situ
tests are the best and most accurate methods and include plate bearing, flat jack and dilatometer tests. However, these
in situ
tests are difficult, time-consuming, expensive and sometimes even impossible. Therefore, many empirical equations have been developed to determine the rock mass deformability indirectly based on various parameters. In this study, the correlation between the deformation modulus values, rock mass rating (RMR) and tunnelling quality index (
Q
) classification has been studied for the Ghezel Ozan dam site in NW Iran. Then, empirical equations are used to calculate the rock mass deformation modulus (
E
m
) based on dilatometer
in situ
tests and rock mass classifications as mentioned above are defined. Finally, the correlation between the equations given in this study and the empirical equations presented by other researchers based on RMR and TQI (
Q
) are also investigated using statistical indicators. According to this analysis, the equation estimated based on RMR has a good compliance with the equations presented by earlier researchers.

Supplementary material:
Supplementary datasets and figures are available at
https://doi.org/10.6084/m9.figshare.c.6139108
An integrated study of core and borehole image data: palaeokarst development in late Permian siliceous dolomites and the structural evolution of the Loppa High from the Gohta discovery well, SW Barents Shelf
Authors: Gutteridge Peter , Garland Joanna , Rameil Niels , Charnock Mike , Polonio Martín Israel
Published: August 2022

The Gohta discovery well 7120/1-3, drilled on the Loppa High in the SW Barents Shelf in northern Norway encountered a meteoric karst system hosted by the late Permian lower Røye Formation that formed during a period of sub-Triassic exposure. The karst system is preserved as a series of interbedded collapse breccias representing formerly open palaeocaves, matrix-rich breccias that represent palaeocave fills with
in situ
and reworked stalactites and stalagmites. Layers of ‘Swiss cheese’ texture are interpreted as zones of dissolution in marine-meteoric mixing zones that may have controlled the levels of palaeocave development within the Gohta structure. These other cavities and the matrix of karst breccias were infilled by layered fine doloarenite matrix interpreted as an internal fill generated by collapse. This was followed by a later matrix of dark grey argillaceous mudstone that is interpreted as an external fill that pre-dates the overlying Klappmyss Formation.

The karst system has undergone complex early diagenesis that includes extensive dissolution, early leaching of silica, the precipitation of pyrite on internal surfaces and sediment and the precipitation of carbonate and silica as speleothems and vadose cement.

A borehole imaging log (BIL) was run across the lower Røye and Triassic Klappmyss formations, including the cored interval that provides a record of inclination and azimuth of the karst fills and
in situ
Røye Formation. The core provides information about the origin and timing of the different karst fills, and the BIL provides information about their azimuth and inclination. This integrated use of core and BIL data allows the incremental tilting history of the Loppa High structure during exposure to be reconstructed. At present, the Gohta structure has a finite tilt of 5–10° to the SE. The plunge of
in situ
stalagmites and stalactites suggests that the karst system was initiated while the Røye Formation was slightly tilted to SE. The dip of karst components and the host stratigraphy indicate several phases of incremental tilting during deposition of the doloarenite and argillaceous mudstone karst matrix. There appears to have been a reversal of tilt of the Gohta structure prior to its onlap by the Klappmyss Formation.
White Box: On the Prediction of Collaborative Filtering Recommendation Systems’ Performance
Authors: Paun Iulia , Moshfeghi Yashar , Ntarmos Nikos
Published: August 2022

Collaborative Filtering (CF) recommendation algorithms are a popular solution to the information overload problem, aiding users in the item selection process. Relevant research has long focused on refining and improving these models to produce better (more effective) recommendations, and has converged on a methodology to predict their effectiveness on target datasets by evaluating them on random samples of the latter. However, predicting the efficiency of the solutions—especially with regard to their time- and resource-hungry training phase, whose requirements dwarf those of the prediction/recommendation phase—has received little to no attention in the literature. This article addresses this gap for a number of representative and highly popular CF models, including algorithms based on matrix factorization, k-nearest neighbors, co-clustering, and slope one schemes. To this end, we first study the computational complexity of the training phase of said CF models and derive time and space complexity equations. Then, using characteristics of the input and the aforementioned equations, we contribute a methodology for predicting the processing time and memory usage of their training phase. Our contributions further include an adaptive sampling strategy, to address the tradeoff between resource usage costs and prediction accuracy, and a framework that quantifies both the efficiency and effectiveness of CF. Finally, a systematic experimental evaluation demonstrates that our method outperforms state-of-the-art regression schemes by a considerable margin, with an overhead that is a small fraction of the overall requirements of CF training.
Query-Guided Prototype Learning with Decoder Alignment and Dynamic Fusion in Few-Shot Segmentation
Authors: Tang Yiming , Yu Yi
Published: August 2022

Few-shot segmentation aims to segment objects belonging to a specific class under the guidance of a few annotated examples. Most existing approaches follow the prototype learning paradigm and generate category prototypes by squeezing masked feature maps extracted from images in the support set. These support prototypes may lead to inaccurate predictions when directly compared with features extracted from the query set due to the considerable distribution discrepancy between support and query features. We propose a query-guided prototype learning architecture to address this problem from two aspects: (i) We propose a cross-alignment loss for training the segmentation decoder. This loss function will help the decoder improve its robustness against the distribution discrepancy between support and query features. (ii) We build a dynamic fusion module to strengthen the original support prototype with another prototype extracted from query features. Experiments show that our method achieves promising results compared to previous prototype learning methods on PASCAL-5

i

and COCO-20

i

datasets.
FPGA-based Acceleration of Time Series Similarity Prediction: From Cloud to Edge
Authors: Kalantar Amin , Zimmerman Zachary , Brisk Philip
Published: August 2022

With the proliferation of low-cost sensors and the Internet of Things, the rate of producing data far exceeds the compute and storage capabilities of today’s infrastructure. Much of this data takes the form of time series, and in response, there has been increasing interest in the creation of time series archives in the past decade, along with the development and deployment of novel analysis methods to process the data. The general strategy has been to apply a plurality of similarity search mechanisms to various subsets and subsequences of time series data to identify repeated patterns and anomalies; however, the computational demands of these approaches renders them incompatible with today’s power-constrained embedded CPUs.

To address this challenge, we present FA-LAMP, an FPGA-accelerated implementation of the Learned Approximate Matrix Profile (LAMP) algorithm, which predicts the correlation between streaming data sampled in real-time and a representative time series dataset used for training. FA-LAMP lends itself as a real-time solution for time series analysis problems such as classification. We present the implementation of FA-LAMP on both edge- and cloud-based prototypes. On the edge devices, FA-LAMP integrates accelerated computation as close as possible to IoT sensors, thereby eliminating the need to transmit and store data in the cloud for posterior analysis. On the cloud-based accelerators, FA-LAMP can execute multiple LAMP models on the same board, allowing simultaneous processing of incoming data from multiple data sources across a network.

LAMP employs a Convolutional Neural Network (CNN) for prediction. This work investigates the challenges and limitations of deploying CNNs on FPGAs using the Xilinx Deep Learning Processor Unit (DPU) and the Vitis AI development environment. We expose several technical limitations of the DPU, while providing a mechanism to overcome them by attaching custom IP block accelerators to the architecture. We evaluate FA-LAMP using a low-cost Xilinx Ultra96-V2 FPGA as well as a cloud-based Xilinx Alveo U280 accelerator card and measure their performance against a prototypical LAMP deployment running on a Raspberry Pi 3, an Edge TPU, a GPU, a desktop CPU, and a server-class CPU. In the edge scenario, the Ultra96-V2 FPGA improved performance and energy consumption compared to the Raspberry Pi; in the cloud scenario, the server CPU and GPU outperformed the Alveo U280 accelerator card, while the desktop CPU achieved comparable performance; however, the Alveo card offered an order of magnitude lower energy consumption compared to the other four platforms. Our implementation is publicly available at https://github.com/aminiok1/lamp-alveo.
A pacing-controlled protocol for frequency-diastolic relations distinguishes diastolic dysfunction specific to a mouse HFpEF model
Authors: Numata Genri , Takimoto Eiki , Kariya Taro , Adachi Yusuke , Tokiwa Hiroyuki , Toyoda Masayuki , Mafune Ryo , Saito Yoshihiro , Nakamura Shun , Ueda Kazutaka , Ikeda Yuichi , Komuro Issei
Published: August 2022

We developed a pacing-controlled PV loop protocol for the assessment of diastolic function at different heart rates in mice, which is a feasible and potent method for the characterization of diastolic dysfunction in a murine HFpEF model whose diastolic dysfunction might be difficult to be detected under resting conditions without pacing.
ECM roles and biomechanics in cardiac tissue decellularization
Authors: Whitehead Kaitlin M. , Hendricks Hanifah K. L. , Cakir Sirin N. , de Castro Brás Lisandra E.
Published: August 2022

Natural biomaterials hold enormous potential for tissue regeneration. The rapid advance of several tissue-engineered biomaterials, such as natural and synthetic polymer-based scaffolds, has led to widespread application of these materials in the clinic and in research. However, biomaterials can have limited repair capacity; obstacles result from immunogenicity, difficulties in mimicking native microenvironments, and maintaining the mechanical and biochemical (i.e., biomechanical) properties of native organs/tissues. The emergence of decellularized extracellular matrix (ECM)-derived biomaterials provides an attractive solution to overcome these hurdles since decellularized ECM provides a nonimmune environment with native three-dimensional structures and bioactive components. More importantly, decellularized ECM can be generated from the tissue of interest, such as the heart, and keep its native macro- and microstructure and tissue-specific composition. These decellularized cardiac matrices/scaffolds can then be reseeded using cardiac cells, and the resulting recellularized construct is considered an ideal choice for regenerating functional organs/tissues. Nonetheless, the decellularization process must be optimized and depends on tissue type, age, and functional goal. Although most decellularization protocols significantly reduce immunogenicity and deliver a matrix that maintains the tissue macrostructure, suboptimal decellularization can change ECM composition and microstructure, which affects the biomechanical properties of the tissue and consequently changes cell-matrix interactions and organ function. Herein, we review methods of decellularization, with particular emphasis on cardiac tissue, and how they can affect the biomechanics of the tissue, which in turn determines success of reseeding and in vivo viability. Moreover, we review recent developments in decellularized ECM-derived cardiac biomaterials and discuss future perspectives.
Developments of Interfacial Measurement Using Cavity Scanning Microwave Microscopy
Authors: Zhang Zhenrong , Wen Huanfei , Li Liangjie , Pei Tao , Guo Hao , Li Zhonghao , Tang Jun , Liu Jun
Published: August 2022

In the field of materials research, scanning microwave microscopy imaging has already become a vital research tool due to its high sensitivity and nondestructive testing of samples. In this article, we review the main theoretical and fundamental components of microwave imaging, in addition to the wide range of applications of microwave imaging. Rather than the indirect determination of material properties by measuring dielectric constants and conductivity, microwave microscopy now permits the direct investigation of semiconductor devices, electromagnetic fields, and ferroelectric domains. This paper reviews recent advances in scanning microwave microscopy in the areas of resolution and operating frequency and presents a discussion of possible future industrial and academic applications.
Effect of Multimedia Health Education on Psychological Burden, Quality of Life Ability, and Self-Efficacy of Congenital Microtia
Authors: Zhou Yanni , Li Xiaoxia , Zhang Meiyi , Lv Guifen , Duan Bing , Tang Zhujun
Published: August 2022

Aims. To investigate the effects of multimedia health education on psychological burden, quality of life, and self-efficacy of patients with congenital microtia. Materials and Methods. Eighty cases of patients with congenital microtia treated and cared for in our hospital from June 2018 to June 2022 were selected according to the numerical table method as retrospective study subjects and divided into 40 cases each in the comparison group and the observation group. The comparison group implemented conventional health education and discharge instruction, and the observation group implemented multimedia health education care to compare the effects of self-efficacy, self-care ability and psychological burden of patients in the two groups. Results. Before care, the two groups had no statistically significant difference in the quality of life scores (

P
>
0.05

). Aftercare, the mental vitality scores, social interaction scores, emotional limitation scores, and mental status of patients in the observation group were significantly higher than those in the comparison group (

P
<
0.05

). Before nursing care, there was no statistically significant difference in the nursing ability and anxiety-depression scores between the two groups (

P
>
0.05

). After nursing care, the health knowledge level, self-care skills, self-care responsibility, and self-concept of the observation group were higher than the comparison group, while the depression-emotional disorder scores were significantly lower than the comparison group (

P
<
0.05

). Conclusion. Routine health education and discharge instruction combined with multimedia health education care can effectively improve the quality of life of patients with congenital microtia, reduce adverse emotions, and improve patients’ sense of self-efficacy.
A Retrospective Analysis of Biological Complications of Dental Implants
Authors: Atieh Momen A. , Almutairi Zainab , Amir-Rad Fatemeh , Koleilat Mohammed , Tawse-Smith Andrew , Ma Sunyoung , Lin Lifeng , Alsabeeha Nabeel H. M.
Published: August 2022

Background and Objective. Several risk factors have been implicated in onset and development of peri-implant diseases. The impact of these factors, however, remains controversial across the different clinical settings and populations. The aim of this retrospective study was to evaluate the risk factors for peri-implant diseases among an Emirati population. Methods. A retrospective analysis of patients aged ≥18 years and having dental implants placed at Dubai Health Authority in 2010. Relevant information related to systemic-, patient-, implant-, site-, surgical- and prosthesis-related factors were collected. The strength of association between the prevalence of peri-implant mucositis and peri-implantitis and each variable was measured by chi-square analysis. A binary logistic regression analysis was performed to identify possible risk factors. Results. A total of 162 patients with 301 implant-supported restorations were included in the study. The age of the patients ranged between 19 and 72 with a mean age of 46.4 ± 11.7 years. The prevalence of peri-implant mucositis at the patient and implant levels were 44.4% and 38.2%, respectively. For peri-implantitis, the prevalence at the patient level was 5.6%, while the prevalence at the implant level was 4.0%. The binary logistic regression identified three risk factors (smoking habits, histories of treated periodontitis and lack of peri-implant maintenance) for peri-implantitis. Conclusion. Within the limitations of this study, smoking habits, history of treated periodontitis and lack of peri-implant maintenance were significant risk factors for peri-implantitis. Early detection of these factors would ensure appropriate planning and care of patients at high risk of developing peri-implant diseases.
Resonant Excitation Analysis of the Absorption Enhancement of L-Shaped Metasurfaces in Midinfrared Band
Authors: Feng Hefa
Published: August 2022

Surface plasmon waveguides have attracted a lot of attention in recent years due to their ability to conduct light in the subwavelength scale. A large number of metallic structures have been used as waveguides and applied in integrated photonic circuits. Among these structures, the metal-insulator-metal surface plasmon waveguide is considered to have unique advantages. Compared to other waveguide forms, it is more compact and thus easier to integrate into optical circuits. For these reasons, we investigated the transmission properties of surface plasmon waves in metal-insulator-metal waveguides and built a MIM absorber consisting of gold and dielectric. We developed an interference model for the MIM absorber and concluded that the interactions between the metal patch and the substrate are not negligible in the near field. In addition, magnetic resonance plays an equally important role in approaching uniform absorption. Absorption spectra with different structural parameters, incidence angles, and polarizations were investigated. A sharp absorption peak was also found to be caused by the Rayleigh anomaly. The study reported in this paper contributes to further understanding of the physical properties of the MIM absorber, and we expect to refine the theoretical model in the future to eliminate the bias caused by near-field coupling.
Field-Based Biochar, Pumice, and Mycorrhizae Application on Dryland Agriculture in Reducing Soil Erosion
Authors: Nugraha Surya Sabda , Sartohadi Junun , Nurudin Makruf
Published: August 2022

Biochar, pumice, and mycorrhizae applications using direct testing methods in the field have not been widely carried out. The application of biochar in this study was used as a conservation material to control runoff and erosion. The research was conducted using a field plot during the peak of the rainy season (March-April) of 2021. The study was conducted in areas where the soil material is dominated by clay (>40%) and steep slope angles (>60%). The cropping pattern at the research site is generally cassava in the dry season and corn in the rainy season. Four 1 × 10 m field plots with corn stands were prepared with biochar, pumice, mycorrhizae, and control treatments. Runoff and sediment measurements were carried out by calculating the volume of water and suspension in the storage tank. The effect of three treatments was observed and measured through some soil characteristics such as bulk density (BD), specific gravity (SG), porosity, organic matter content (OM), cation exchange capacity (CEC), and aggregate stability. The highest rainfall in March and April reached 441 mm/month, with the highest intensity reaching 150 mm/week. Under intense rainfall, biochar application provides better performance than pumice and mycorrhizae. Runoff reduction from biochar is the highest, with 51.67%. On the other hand, pumice and mycorrhizae show a lower effectivity in decreasing runoff with 40.15% and 37.92%, respectively. The effectivity on lowering runoff translates to each ameliorant’s performance in reducing soil loss. Biochar decreases soil loss by 50.78%, while pumice and mycorrhizae decrease soil loss by 37.9% and 26.26%. The application of biochar reduced the rate of erosion by altering soil characteristics. Biochar application provides better soil characteristics by reducing BD and SG while at the same time increasing the porosity, OM, CEC, and aggregate stability. The changes provided by biochar can provide means to both soil conservation and increase in soil productivity.
Stress Analysis of Concrete Materials Based on Finite Element Analysis
Authors: Yang Rui , Yuan Shengli
Published: August 2022

China’s construction industry standard “technical code for concrete special-shaped column structure” has been implemented since 2006, and the use of special-shaped walls in the actual construction of buildings is more common. The cross-section wall can effectively reduce the protruding angle of the building, thereby expanding the effective building area of the room, reducing the proportion of the building components themselves, and making the structure layout of the building more beautiful. In order to analyze the dynamic characteristics of eccentrically compressed prestressed concrete beams, the finite element simulation of different reinforcement parameters, longitudinal reinforcement diameter, and reinforcement ratio was constructed. Through comparison, the load flexibility curve change law of concrete stress cloud and reinforcement stress cloud under various working conditions and the influence change law of test mold are studied, and the influence change law of dynamic characteristics under the above four conditions and unidirectional bias pressure is clarified.
Magnetic-Acoustic Feature Extraction and Damage Fusion Evaluation of 45 Steel Specimens during Fatigue Process for Remanufacturing
Authors: Liu Tao , Shen Chaoyang , Lei Jingfa , Yin Zhiqiang , Sun Hong , Wu Jingxiong
Published: August 2022

To clarify the changes of the magnetic-acoustic features of 45 steel specimens during fatigue damage, an experimental platform was built to carry out magnetic memory and acoustic emission detection. The magnetic memory and acoustic emission signals of specimens in different damage states were collected, and the multi-scale entropy characteristics of magnetic memory signals, as well as the wavelet packet energy spectrum and singularity index characteristics of acoustic emission signals, were further extracted. A magnetic-acoustic feature fusion and damage assessment model was constructed by using Naive Bayes method. Results show that the average value of multi-scale entropy of normal magnetic field intensity Hp (y) increases gradually with the increase of fatigue cycles, and the average value of multi-scale entropy of magnetic field intensity gradient K gradually decreases. The cumulative ringing count and energy spectrum (proportion of frequency band 1) of acoustic emission signals decrease with the increase of fatigue cycles, while the amplitude singularity index gradually increases. The established model has high evaluation accuracy, and the conclusions of this paper can provide basic methods and data support for fatigue damage evaluation of remanufactured components.
Carboxymethylation of Desmodium styracifolium Polysaccharide and Its Repair Effect on Damaged HK-2 Cells
Authors: Tang Gu-Hua , Liu Jing-Hong , Sun Xin-Yuan , Ouyang Jian-Ming
Published: August 2022

Objective. Desmodium styracifolium is the best traditional medicine for treating kidney calculi in China. This study is aimed at increasing the carboxyl (-COOH) content of D. styracifolium polysaccharide (DSP0) and further increasing its antistone activity. Methods. DSP0 was carboxymethylated with chloroacetic acid at varying degrees. Then, oxalate-damaged HK-2 cells were repaired with modified polysaccharide, and the changes in biochemical indices before and after repair were detected. Results. Three modified polysaccharides with 7.45% (CDSP1), 12.2% (CDSP2), and 17.7% (CDSP3) -COOH are obtained. Compared with DSP0 (-COOH

content
=
1.17
%

), CDSPs have stronger antioxidant activity in vitro and can improve the vitality of damaged HK-2 cells. CDSPs repair the cell morphology and cytoskeleton, increase the cell healing ability, reduce reactive oxygen species and nitric oxide levels, increase mitochondrial membrane potential, limit autophagy level to a low level, reduce the eversion of phosphatidylserine in the cell membrane, weaken the inhibition of oxalate on DNA synthesis, restore cell cycle to normal state, promote cell proliferation, and reduce apoptosis/necrosis. Conclusion. The carboxymethylation modification of DSP0 can improve its antioxidant activity and enhance its ability to repair damaged HK-2 cells. Among them, CDSP2 with medium -COOH content has the highest activity of repairing cells, whereas CDSP3 with the highest -COOH content has the highest antioxidant activity. This difference may be related to the active environment of polysaccharide and conformation of the polysaccharide and cell signal pathway. This result suggests that Desmodium styracifolium polysaccharide with increased -COOH content may have improved potential treatment and prevention of kidney calculi.
MFOC-CliqueNet: A CliqueNet-Based Optimal Combination of Multidimensional Features Classification Method for Large-Scale Laser Point Clouds
Authors: Wang Lei , Zhang Zhiyong , Li Xiaonan , He Yueshun
Published: August 2022

As large-scale laser 3D point clouds data contains massive and complex data, it faces great challenges in the automatic intelligent processing and classification of large-scale 3D point clouds. Aiming at the problem that 3D point clouds in complex scenes are self-occluded or occluded, which could reduce the object classification accuracy, we propose a multidimension feature optimal combination classification method named MFOC-CliqueNet based on CliqueNet for large-scale laser point clouds. The optimal combination matrix of multidimension features is constructed by extracting the three-dimensional features and multidirectional two-dimension features of 3D point cloud. This is the first time that multidimensional optimal combination features are introduced into cyclic convolutional networks CliqueNet. It is important for large-scale 3D point cloud classification. The experimental results show that the MFOC-CliqueNet framework can realize the latest level with fewer parameters. The experiments on the Large-Scale Scene Point Cloud Oakland dataset show that the classification accuracy of our method is 98.9%, which is better than other classification algorithms mentioned in this paper.
Practice towards Hepatitis B Virus Infection Prevention and Its Associated Factors among Undergraduate Students at Hawassa University College of Medicine and Health Sciences, Hawassa, Sidama, Ethiopia, 2021: Cross-Sectional Study
Authors: Aynalem Amdehiwot , Deribe Bedilu , Ayalew Mohammed , Mamuye Abyalew , Israel Eskinder , Mebratu Andualem , Assefa Dawit Getachew
Published: August 2022

Background. Hepatitis B virus infection is a major global health burden accounting for 2.7% of all deaths globally. Being part of the health care system, the risk of exposure to hepatitis B viral infection among medical and health science students is found to be high. In Ethiopia, particularly in this study area, very little is known about the practice of students towards hepatitis B virus infection prevention and its associated factors. Objective. The aim of this study was to assess the practice towards hepatitis B virus infection prevention and its associated factors among undergraduate students at Hawassa University College of Medicine and Health Sciences, Hawassa, Ethiopia, 2021. Methods and Materials. An institution-based cross-sectional study was conducted from May 15 to June 15, 2021, among undergraduate students who had clinical exposure. The 404 sampled participants were recruited using a systematic random sampling technique. Data was collected using a structured self-administered questionnaire. Data was entered into EpiData version 4.6.0 and was exported to SPSS version 25 for analysis. Association between the dependent and independent variables was computed using the bivariate and multivariate logistic regression model. Odds ratio was calculated. Results were interpreted as significant if

P

value is <0.05 at 95% CI. Result. This study revealed that 277 (69.9%) of the students were in the age group of 20-24 years and 266 (67.2%) were males. Out of 396 participants, about half 199 (50.3%) 95% CI (0.452–553) had a good practice towards hepatitis B virus infection prevention. Only 43.4% of the study participants had been completely vaccinated against hepatitis B virus. Age (20-24 years) (

AOR
=
2.736

), 95% CI (1.130-6.625), and good knowledge (

AOR
=
1.990

), 95% CI (1.207-3.282) were factors significantly associated with the practice towards hepatitis B virus infection prevention. Conclusion and Recommendation. The current study showed that about half of the study participants had good practice towards hepatitis B virus infection prevention but more than half were not completely vaccinated against HBV. Age and knowledge were factors significantly associated. It is recommended to give training for students on hepatitis B virus infection prevention. It is also advisable to screen and vaccinate students before they start their clinical attachments.
Intervention Effect of Group Counseling Based on Positive Psychology on Psychological Crisis of College Student
Authors: Zhang Zixi , Li Yabo , Sun Sailan , Tang Ziye
Published: August 2022

Objective. To analyze the intervention effect of group counseling based on positive psychology on psychological crisis of college student. Method. SCL-90 mental health screening was performed on second-year students in a college by cluster stratification. Among the detected students, 210 were included into the group after brief interview and randomly assigned to the experimental group and the control group. The control group was given conventional intervention measures, and the experimental group was given group counseling according to the interview results. One week before the start, on the day of the end of the intervention and 3 months after the end of the intervention, the mental health level of all subjects was evaluated by symptom self-rating scale, general well-being scale, and adolescent mental resilience scale. Results. The scores of two groups were different at different time points during the intervention. With the extension of time, the score of the self-rating symptom scale in the experimental group decreased significantly, while the total score of the self-rating symptom scale in the control group increased, with statistical significance

P
<
0.05

. Before the intervention, there was no significant difference in general well-being between the two groups

P
>
0.05

. At 3 months after the end of intervention, the total score of the general well-being scale in the experimental group increased, while that in the control group decreased, with statistical significance

P
<
0.05

. Three months after the end of the intervention, the total score of adolescent mental resilience scale in the experimental group increased. Conclusion. Group counseling from the perspective of positive psychology can effectively improve the mental health status of medical students with psychological crisis and improve their mental resilience.
Efficacy of Postoperative Analgesia by Erector Spinal Plane Block after Lumbar Surgery: A Systematic Review and Meta-analysis of Randomized Controlled Trials
Authors: Xiao Xiao , Zhu Tingting , Wang Lin , Zhou Hongmei , Zhang Yanli
Published: August 2022

Background. In recent years, erector spinae plane block (ESPB) has been increasingly used as a new regional block technique for postoperative analgesia; however, little is known on its benefits. Therefore, we performed a systematic review and meta-analysis to investigate the efficacy and safety of ESPB in lumbar spine surgery. Methods. Databases including PubMed, Embase, Cochrane Library, and Web of Science were systematically searched for randomized controlled trials (RCTs) comparing ESPB with no block in lumbar spine surgery until September 30, 2021. The primary outcome was opioid consumption after surgery. The Cochrane Collaboration’s tool for assessing the risk of bias was used to evaluate the quality of included studies. Results. Fifteen RCTs involving 980 patients were included in the study. Opioid consumption 24 hours after surgery was significantly lower in the ESPB group standardized mean difference (

SMD
=
−
2.27

, 95% confidence interval (95% CI) (-3.21, -1.32);

p
<
0.01

). ESPB reduced pain scores at rest and on movement within 48 hours after surgery and the incidence of the postoperative rescue analgesia (

RR
=
0.32

, 95% CI (0.31, 0.80);

p
=
0.02

), while it significantly prolonged time to first rescue analgesia (

SMD
=
4.87

, 95% CI (2.84, 6.90);

p
<
0.01

). Moreover, significantly better patient satisfaction was associated with ESPB (

SMD
=
1.89

, 95% CI (1.03, 2.74);

p
<
0.01

). Conclusion. EPSB provides effective and safe postoperative analgesia after lumbar spine surgery.
Efficacy and Safety of Treating Refractory Bone and Soft Tissue Sarcoma with Anlotinib in Different Treatment Patterns
Authors: Cai Meng , Zhu Jie , Zhou Guangxin
Published: August 2022

Background and Aim. Anlotinib, a novel tyrosine kinase inhibitor, has demonstrated encouraging antitumor activity in bone and soft tissue sarcomas in several prospective clinical trials. This retrospective study is aimed at analyzing the clinical efficacy and safety of treating refractory bone and soft tissue sarcoma with anlotinib in different treatment patterns. Methods. The medical data of 47 patients with refractory bone and soft tissue sarcoma, who received anlotinib from January 2019 to December 2020, were retrospectively collected. The overall response rate (ORR) and disease control rate (DCR) were evaluated according to the solid tumor response evaluation version 1.1 standard. The progression-free survival (PFS), overall survival (OS), and adverse reactions were recorded. Results. A total of 44 patients, including 13 with osteosarcoma and 31 with soft tissue sarcoma, were enrolled in this study. Among patients with osteosarcoma, no patients achieved complete response (CR) or partial response (PR), while seven patients (54%) had stable disease (SD). Besides, the median PFS (m-PFS) was 4.4 months, and the median OS (m-OS) was 15.7 months. Among patients with soft tissue sarcoma, the ORR and DCR were 19% and 71%, respectively. The median m-PFS was 5.4 months, and m-OS was 17.9 months. Anlotinib plus chemotherapy had a higher ORR compared with anlotinib monotherapy (6% vs. 38%,

P
=
0.047

). The most common grade 3/4 adverse reactions were pneumothorax (5%) and pleural effusion (5%), and no treatment-related deaths occurred. Conclusions. Anlotinib alone showed encouraging efficacy and favorable tolerability in refractory bone and soft tissue sarcoma. Anlotinib plus chemotherapy did not show a significant clinical benefit compared with anlotinib alone. Anlotinib showed better tumor control when used as first-line drug treatment in refractory bone and soft tissue sarcoma.
Potential Mechanisms of Biejiajian Pill in the Treatment of Diabetic Atherosclerosis Based on Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation
Authors: Zhu Rong , Du Bingzhao , Yuan Jiayao , Yan Shuxun , Shao Mingyi , Sang Feng , Bi Qian , Wang Zhongrui , Zhen Qian , Fu Yu
Published: August 2022

Background. Biejiajian pill (BJJP), a classical traditional Chinese formula, has been reported that it has an effective treatment for diabetic atherosclerosis in recent years, but its underlying mechanisms remain elusive. The study aimed to explore the potential mechanisms of BJJP on diabetic atherosclerosis by integrating network pharmacology, molecular docking, and molecular dynamics simulation. Methods. The active components of BJJP were collected by TCMSP and TCMID, and then the potential targets were obtained from the SwissTargetPrediction database. The targets related to diabetic atherosclerosis were identified from the GeneCards and OMIM databases. The intersection of the potential targets regulated by active components of BJJP and the targets of diabetic atherosclerosis were common targets, which were visualized by the Venn diagram. The common targets were imported into the STRING database to construct a protein-protein interaction (PPI) network. The network of “Medicine-Compound-Target” was constructed with Cytoscape 3.7.1 software. GO functional enrichment analysis and KEGG pathway enrichment analysis were performed using the DAVID database and visualized through bioinformatics. The intersecting targets were input into Cytoscape 3.7.1 software, and the Network Analyzer tool was employed to screen out the key targets. Then molecular docking was used to verify the binding affinity between the active compounds and the key targets, and molecular dynamics simulation was used to investigate the stability of the binding models. Results. A total of 81 active components, 186 targets of BJJP, and 4041 targets of diabetic atherosclerosis were obtained. Furthermore, 121 overlapping targets were identified. GO functional enrichment analysis revealed that these targets were correlated with the oxidation-reduction process, negative regulation of apoptotic process, inflammatory response, and other biological processes. The results of the KEGG pathway enrichment analysis showed that the common targets mainly participated in proteoglycans in cancer, PPAR signaling pathway, adherens junction, insulin resistance, HIF-1 signaling pathway, PI3K-Akt signaling pathway, etc. The results of molecular docking confirmed that the core active components in BJJP could bind well to the key targets. Results from molecular dynamics simulation showed that the binding energies of AKT1-Luteolin, MMP9-quercetin, and MMP9-luteolin complexes were −28.93 kJ·mol−1, −37.12 kJ·mol−1, and −62.91 kJ·mol−1, respectively. Conclusion. The study revealed that BJJP is characterized as multicomponent, multitarget, and multipathway to treat diabetic atherosclerosis, which is helpful to provide ideas and a basis for pharmacological research and clinical application in the future.
Testing the Augmented Fama–French Six‐Factor Asset Pricing Model with Momentum Factor for Borsa Istanbul
Authors: Doğan Mesut , Kevser Mustafa , Leyli Demirel Bilge
Published: August 2022

This study aims to test the validity of the Fama–French Asset Pricing Model, which has become a six‐factor along with the inclusion of the momentum factor, in terms of Borsa Istanbul. In this context, nested asset pricing models were assessed, and different estimators were developed to determine which of the models explains the stock returns more strongly. The returns (more than the risk‐free interest rate) of 24 different portfolios and a total of 9,504 portfolios for 396 weeks, throughout October 2013–May 2021, are utilized based on the BV/MV, profitability, investment, and momentum factors. The results obtained from the research study indicate the Fama–French Six‐Factor Asset Pricing Model (FF6F) as the most effective model in explaining stock returns for Borsa Istanbul. For investors, the momentum factor is the one that needs to be regarded and allows higher returns to be obtained, and the necessity of considering it before making investment decisions is one of the practical contributions of the research study. Determining the momentum factor as a factor that should be considered upon making investment decisions would constitute the contribution of the research study to the literature.
Sparrow Search Algorithm-Optimized Long Short-Term Memory Model for Stock Trend Prediction
Authors: Liu Feiyang , Qin Panke , You Junru , Fu Yanyan
Published: August 2022

The long short-term memory (LSTM) network is especially suitable for dealing with time series-related problems, which has led to a wide range of applications in analyzing stock market quotations and predicting future price trends. However, the selection of hyperparameters in LSTM networks was often based on subjective experience and existing research. The inability to determine the optimal values of the parameters results in a reduced generalization capability of the model. Therefore, we proposed a sparrow search algorithm-optimized LSTM (SSA-LSTM) model for stock trend prediction. The SSA was used to find the optimal hyperparameters of the LSTM model to adapt the features of the data to the structure of the model, so as to construct a highly accurate stock trend prediction model. With the Shanghai Composite Index stock data in the last decade, the mean absolute percentage error, root mean square error, mean absolute error, and coefficient of determination between stock prices predicted by the SSA-LSTM method and actual prices are 0.0093, 41.9505, 30.5300, and 0.9754. The result indicates that the proposed model possesses higher forecasting precision than other traditional stock forecasting methods and enhances the interpretability of the network model structure and parameters.
Spatial Efficiency Evaluation for Outdoor Environment in High-Rise Residential Area: An Data Envelopment Analysis Approach
Authors: Li Qian , Zhang Xiaorui , Wan Xiaoyong
Published: August 2022

In this paper, the data envelopment analysis (DEA) approach is put forward to deal with the spatial efficiency evaluation issue for the outdoor environment in high-rise residential (HRR) areas in China. The principles of quantifiable and objectivity are utilized to empirically research for the area of 10 high-rise residential houses. For the outdoor environment in HRR areas, our aim is to take appropriate optimization suggestions that not only satisfy the different conditions of the outdoor environment but also improve the efficiency of site space. By resorting to the data envelopment analysis technique, some practical methods are acquired for evaluating the outdoor environment for HRR areas.
Research on the Audit Prediction Model of “Special Bonds + PPP” Project based on Machine Learning
Authors: Fang Qianru
Published: August 2022

This paper aims at the whole-process tracking audit problem of “special bonds + PPP” mode (hereinafter referred to as “special bonds + PPP”) in public infrastructure construction projects and establishes an audit evaluation prediction model based on the theory and method of machine learning. Firstly, based on expert interviews and the actual working process of “special bonds + PPP,” the comprehensive evaluation index system of the whole process tracking audit is established. Secondly, innovate audit technology methods and apply machine learning theories and methods such as support vector machine, back propagation neural network, multinomial logistic regression, and random forest to the whole tracking audit of “special bonds + PPP.” Finally, the real case evaluation sample data are selected, and the four established models, that is, SVM, BP, Multinom, and RF, are trained and predicted. After comparative analysis, the RF model with the highest accuracy is selected as the evaluation prediction model.
Research on the Cultivation of College English Listening, Speaking, Reading, and Writing Ability by VR Technology
Authors: Zeng Zhen
Published: August 2022

With the development of VR technology, it is possible to apply innovation to English teaching, and realize new breakthroughs and innovations in English teaching. In the background of information technology widely used in teaching, VR technology has become a very popular new technology because it can promote teaching better and make teaching effect more obvious; Through VR technology in English learning in listening, speaking, reading, and writing mode, we summarize the application of this technology in education industry at home and abroad, and put forward the purpose and significance of this research. Firstly, through the listening, speaking, reading, and writing mode of VR technology in English learning; In order to better evaluate the performance comparison under different modes, the score and weight distribution method are carried out for listening, speaking, reading, and writing. Secondly, by evaluating the value of E(X) in the nonexisting combination mode, the corresponding optimal proportion distribution scheme can be obtained. Finally, by comparing the scores of 8 groups of people under VR technology and statistical learning mode, the results show that VR technology has obvious advantages in improving listening, speaking, reading, and writing scores.