bluuebunny/crossref_metadata_2025 huggingface dataset:
Correlation analysis of financial conflicts of interest and favourability of results or conclusions in addiction medicine systematic reviews and meta-analysis
Authors: Vassar Matthew , Shepard Samuel , Demla Simran , Tritz Daniel
Published: August 2022

Objective

To quantify conflicts of interest, assess the accuracy of authors self-reporting them, and examine the association between conflicts of interest and favourability of results and discussions in addiction medicine systematic reviews.

Design

A search was performed on Medline (Ovid) from January 2016 to 25 April 2020 to locate systematic reviews and meta-analyses focused on treatments of addiction disorders using a systematic search strategy. Data were extracted from each systematic review, including conflict of interest statements, authorship characteristics and the favourability of the results/conclusion sections. A search algorithm was used to identify any undisclosed conflicts of interest on the Open Payments Database (Dollars for Docs), Dollars for Profs, Google Patents/United States Patent and Trade Office, and prior conflict of interest statements in other published works from these authors.

Results

The search identified 127 systematic reviews, representing 665 unique authors. Of the 127 studies, 81 reported no authors with conflicts of interest, 28 with 1 or more conflict, and 18 had no conflict of interest statement. Additional non-disclosed conflicts of interest were found for 34 authors. There were 69 reviews that had at least one author with a conflict of interest. Of the 69 reviews, 14 (20.3%) reported favourable results and 26 (37.7%) reported favourable discussion/conclusions with no statistically significant association. A subanalysis was performed on publications with only US authors (51) with 35 (68.9%) having at least 1 conflict of interest. US authored studies that had a conflict of interest favoured the results (p = <0.001) and discussion/conclusion (p = 0.018) more often.

Conclusion

Although multiple undisclosed financial conflicts of interest were found, there was no correlation with the favourability of the results or discussion/conclusions across all addiction medicine systematic reviews. Further research needs to be done on US-based publications and encourage disclosure systems worldwide to provide more accurate reporting.
Association of learning environment and self-directed learning ability among nursing undergraduates: a cross-sectional study using canonical correlation analysis
Authors: Tang Li-Qing , Zhu Li-Jun , Wen Li-Ying , Wang An-Shi , Jin Yue-Long , Chang Wei-Wei
Published: August 2022

Objectives

This study explores the relationship between the perception of the learning environment and self-directed learning (SDL) ability among nursing undergraduates.

Design, setting and participants

A cross-sectional study was conducted in December 2020 with 1096 junior and senior undergraduate nursing students (aged 16–22) from Wannan Medical College in Anhui Province, China.

Outcome measures

The Chinese version of the Dundee Ready Educational Environment Measure questionnaire and a validated Chinese version of college students’ SDL ability scale were used to assess students’ perceptions about their learning environment and their SDL ability. Canonical correlation analysis was performed to evaluate their correlation.

Results

The total score for the learning environment was 120.60 (scoring rate: 60.30%), and the score for SDL ability was 89.25 (scoring rate: 63.75%). Analysis indicated that the first canonical correlation coefficient was 0.701 and the contribution rate was 94.26%. The perception of the learning environment was mainly determined by students’ perception of learning (SPL) and academic self-perceptions (SASP), with SDL ability mainly determined by self-management ability and cooperative learning ability. SPL and SASP were positively correlated with self-management ability and cooperative learning ability. Multiple linear regression analysis revealed that SPL, SASP, students’ perceptions of atmosphere and students’ social self-perceptions had a significant impact on SDL ability.

Conclusions

The SDL ability of nursing undergraduates was not high. SPL and SASP were positively correlated with self-management ability and cooperative learning ability. Nursing educators can improve students’ SDL ability by changing their learning environment, using, for example, new student-centred teaching methods.
Residual disease after primary surgery for advanced epithelial ovarian cancer: expert elicitation exercise to explore opinions about potential impact of publication bias in a planned systematic review and meta-analysis
Authors: Bryant Andrew , Grayling Michael , Hiu Shaun , Gajjar Ketankumar , Johnson Eugenie , Elattar Ahmed , Vale Luke , Craig Dawn , Naik Raj
Published: August 2022

Objectives

We consider expert opinion and its incorporation into a planned meta-analysis as a way of adjusting for anticipated publication bias. We conduct an elicitation exercise among eligible British Gynaecological Cancer Society (BGCS) members with expertise in gynaecology.

Design

Expert elicitation exercise.

Setting

BGCS.

Participants

Members of the BGCS with expertise in gynaecology.

Methods

Experts were presented with details of a planned prospective systematic review and meta-analysis, assessing overall survival for the extent of excision of residual disease (RD) after primary surgery for advanced epithelial ovarian cancer. Participants were asked views on the likelihood of different studies (varied in the size of the study population and the RD thresholds being compared) not being published. Descriptive statistics were produced and opinions on total number of missing studies by sample size and magnitude of effect size estimated.

Results

Eighteen expert respondents were included. Responders perceived publication bias to be a possibility for comparisons of RD <1 cm versus RD=0 cm, but more so for comparisons involving higher volume suboptimal RD thresholds. However, experts’ perceived publication bias in comparisons of RD=0 cm versus suboptimal RD thresholds did not translate into many elicited missing studies in Part B of the elicitation exercise. The median number of missing studies estimated by responders for the main comparison of RD<1 cm versus RD=0 cm was 10 (IQR: 5–20), with the number of missing studies influenced by whether the effect size was equivocal. The median number of missing studies estimated for suboptimal RD versus RD=0 cm was lower.

Conclusions

The results may raise awareness that a degree of scepticism is needed when reviewing studies comparing RD <1 cm versus RD=0 cm. There is also a belief among respondents that comparisons involving RD=0 cm and suboptimal thresholds (>1 cm) are likely to be impacted by publication bias, but this is unlikely to attenuate effect estimates in meta-analyses.
Mental health status of healthcare workers assisted in Hubei during the initial outbreak of COVID-19 and their influencing factors: a prospective cohort study
Authors: Zhou Peng , Du Na , Xiao Yu , Li Yunge , Li Chunya , Geng Ting
Published: August 2022

Objectives

The COVID-19 outbreak beginning in late 2019 has resulted in negative emotions among the public. However, many healthcare workers risked their lives by voluntarily travelling to the worst-hit area, Hubei Province, to support antipandemic work. This study explored the mental health changes in these healthcare workers and tried to discover the influencing factors.

Design

A longitudinal online survey was begun on 8 February 2020, using the snowball sampling method, and this first phase ended on 22 February 2020 (T1). The follow-up survey was conducted from 8 February to 22 February 2021 (T2).

Setting

Healthcare workers from outside of the Hubei area who went to the province to provide medical assistance.

Participants

963 healthcare workers who completed both surveys.

Measures

Self-Rating Scale of Sleep (SRSS), Generalised Anxiety Scale (GAD-7) and 9-item Patient Health Questionnaire (PHQ-9).

Results

There were no significant differences in the SRSS scores or in the GAD-7 scores between T1 and T2 (t=0.994, 0.288; p>0.05). However, the PHQ-9 score at T2 was significantly higher than the score at T1 (t=−10.812, p<0.001). Through multiple linear regression analysis, we found that the following traits could predict higher GAD-7 and PHQ-9 scores at T2: male sex, single marital status, occupation of nurse, lower professional technical titles, healthcare workers having a history of psychosis, treating seriously ill patients, having relatively poor self-perceived health, caring for patients who died and having family members who had been infected with COVID-19.

Conclusions

The results indicate that the depression levels of these special healthcare workers increased in the long term, and the initial demographics and experiences related to the pandemic played an important role in predicting their long-term poor mental health. In the future, more appropriate psychological decompression training should be provided for these special healthcare workers.
SARS-CoV-2 tests, confirmed infections and COVID-19-related hospital admissions in children and young people: birth cohort study
Authors: Hardelid Pia , Favarato Graziella , Wijlaars Linda , Fenton Lynda , McMenamin Jim , Clemens Tom , Dibben Chris , Milojevic Ai , Macfarlane Alison , Taylor Jonathon , Cunningham Steven , Wood Rachael
Published: August 2022

Background

There have been no population-based studies of SARS-CoV-2 testing, PCR-confirmed infections and COVID-19-related hospital admissions across the full paediatric age range. We examine the epidemiology of SARS-CoV-2 in children and young people (CYP) aged <23 years.

Methods

We used a birth cohort of all children born in Scotland since 1997, constructed via linkage between vital statistics, hospital records and SARS-CoV-2 surveillance data. We calculated risks of tests and PCR-confirmed infections per 1000 CYP-years between August and December 2020, and COVID-19-related hospital admissions per 100 000 CYP-years between February and December 2020. We used Poisson and Cox proportional hazards regression models to determine risk factors.

Results

Among the 1 226 855 CYP in the cohort, there were 378 402 tests (a rate of 770.8/1000 CYP-years (95% CI 768.4 to 773.3)), 19 005 PCR-confirmed infections (179.4/1000 CYP-years (176.9 to 182.0)) and 346 admissions (29.4/100 000 CYP-years (26.3 to 32.8)). Infants had the highest COVID-19-related admission rates. The presence of chronic conditions, particularly multiple types of conditions, was strongly associated with COVID-19-related admissions across all ages. Overall, 49% of admitted CYP had at least one chronic condition recorded.

Conclusions

Infants and CYP with chronic conditions are at highest risk of admission with COVID-19. Half of admitted CYP had chronic conditions. Studies examining COVID-19 vaccine effectiveness among children with chronic conditions and whether maternal vaccine during pregnancy prevents COVID-19 admissions in infants are urgently needed.
Incidence of adverse perinatal outcomes and risk factors among women with pre-eclampsia, southern Ethiopia: a prospective open cohort study
Authors: Jikamo Birhanu , Adefris Mulat , Azale Telake , Gelaye Kassahun Alemu
Published: August 2022

Background

In Ethiopia, in 2021, more than 80% of all newborn deaths were caused by preventable and treatable conditions. This study aimed to measure the incidence of adverse perinatal outcomes and risk factors among women with pre-eclampsia in the Sidama region of southern Ethiopia.

Methods

A prospective open cohort study was conducted from 8 August 2019 to 1 October 2020. We enrolled 363 women with pre-eclampsia and 367 normotensive women at ≥20 weeks of gestation and followed them until the 37th week. We then followed them until the seventh day after delivery up to the last perinatal outcome status was ascertained. A log-binomial logistic regression model was used to estimate the incidence of adverse perinatal outcomes and its risk factors among women with pre-eclampsia. Relative risk (RR) with a 95% CI was reported. A p<0.05 was considered statistically significant.

Results

There were 224 adverse perinatal outcomes observed in the 363 women with pre-eclampsia compared with 136 adverse perinatal outcomes in the 367 normotensive women (p<0.001). There were 23 early neonatal deaths in the pre-eclampsia group compared with six deaths in the normotensive group (p<0.001). There were 35 perinatal deaths in the pre-eclampsia group compared with 16 deaths in the normotensive group (p<0.05). Women with severe features of pre-eclampsia had a 46% (adjusted RR 1.46, 95% CI 1.38 to 2.77) higher risk for adverse perinatal outcomes compared with women without severe features of pre-eclampsia.

Conclusions

In this study, more adverse perinatal outcomes occurred among women with pre-eclampsia after controlling for confounders. A higher perinatal outcome observed among women with pre-eclampsia, especially among women with severe features of pre-eclampsia, and those admitted to hospital at <34 weeks. This paper highlights the significantly elevated perinatal risks associated with pre-eclampsia, especially when it has severe features.
Echocardiographic features of left ventricular dysfunction and outcomes in chronic kidney disease
Authors: Ou Shuo-Ming , Chao Chieh-Ju , Tsai Ming-Tsun , Lee Kuo-Hua , Tseng Wei-Cheng , Bin Pin-Jie , Lin Yao-Ping , Hsu Chien-Yi , Tarng Der-Cherng
Published: August 2022

Objective

Heart failure (HF) imposes a substantial burden and the prevalence of HF is high in patients with chronic kidney disease (CKD). HF results in multiple hospital admissions, but whether HF subtypes worsen long-term outcomes and renal function in patients with CKD remains inconclusive.

Methods

The study comprised 10 904 patients with CKD aged ≥20 years who underwent echocardiography between 1 January 2011 and 31 December 2018. The patients were stratiﬁed into four groups: non-HF, HF with reduced ejection fraction (HFrEF), HF with mildly reduced ejection fraction (HFmrEF) and HF with preserved ejection fraction (HFpEF). The primary end points were all-cause mortality, major adverse cardiovascular events (MACEs) and adverse renal outcomes.

Results

In inverse probability of treatment weighting-adjusted method, the risk of all-cause mortality and MACEs relative to the non-HF group was greatest in the HFrEF group (HR 3.18 (95% CI 2.57 to 3.93) and HR 3.83 (95% CI 3.20 to 4.59)), followed by the HFmrEF (HR 2.75 (95% CI 2.22 to 3.42) and HR 3.08 (95% CI 2.57 to 3.69)) and HFpEF (HR 1.85 (95% CI 1.59 to 2.15) and HR 2.43 (95% CI 2.16 to 2.73) groups. In addition, the HFrEF group had the greatest risks of end-stage renal disease (HR 2.58 (95% CI 1.94 to 3.44)) compared with other groups.

Conclusions

HF is associated with subsequent worse clinical outcomes, which may be more pronounced in patients with HFrEF, followed by those with HFmrEF and those with HFpEF relative to non-HF group.
Developing future clinical leaders in patient safety: the Irish experience
Authors: Murray Kieran , Fitzgerald Eoin , Ali Ahmeda , Crosby David , O'Reilly Susanne , Lachman Peter
Published: August 2022

Introduction

It is 20 years since the Institute of Medicine advocated a national approach to improve care and patient safety. Patient safety infrastructure has greatly improved in certain countries. In Ireland, patient safety infrastructure is in ongoing development. To contribute to this, the Royal College of Physicians of Ireland/International Society for Quality in Healthcare Scholar in Residence Programme was launched in 2016. This programme aims to improve patient safety and develop a movement of future clinician leaders to drive improvements in patient safety and the quality of care.

Methods

Doctors in postgraduate training complete a year-long immersive mentorship. This involves monthly group meetings with key patient safety opinion makers, one-on-one mentorship, leadership courses, conference attendance and presentations. Each scholar undertakes a quality improvement (QI) project.

Results

A QI project was associated with a decrease in caesarean section rates from 13.7% to 7.6% (p=0.0002) among women in spontaneous labour at term with a cephalic presentation. Other projects are ongoing.

Conclusion

Medical error, patient safety and QI must be addressed comprehensively at both undergraduate and postgraduate level. We believe the Irish mentorship programme will help to change the paradigm and improve patient safety.
Thermalon mediated AdS to dS phase transitions in Einstein–Gauss–Bonnet-massive gravity
Authors: Ponglertsakul Supakchai , Channuie Phongpichit , Samart Daris
Published: August 2022

In this work, gravitational phase transition emerging from anti de-Sitter (AdS) to de-Sitter (dS) vacua in Einstein–Gauss–Bonnet-massive gravity (EGBMG) is considered. We determine the location of thermalon (a static bubble solution in Euclidean space) which exists in casually connected two regions of the spacetime. The thermalon plays a major role in gravitational phase transition by inducing the decay of the negative effective cosmological constant to the positive one due to the higher-order gravity effects. The thermodynamics phase space of the Hawking temperature and free energy is investigated in details. We find that the free energy of the thermalon is always negative at the maximum of Hawking temperature for all possible values of the Gauss–Bonnet couplings. This means that the phase transition mediated by thermalon from AdS to dS asymptotics is inevitable according to the presence of the massive gravity. More importantly, the parameters of the massive gravity behave similarly to the order parameters in the phase transition instead of the Gauss–Bonnet coupling.
Thermal stability with emission energy and Joule–Thomson expansion of regular BTZ-like black hole
Authors: Ditta Allah , Tiecheng Xia , Mustafa G. , Yasir Muhammad , Atamurotov Farruh
Published: August 2022

We investigate the thermodynamic properties and Joule–Thomson expansion for conical or BTZ-like black holes. To analyze the thermal stability, we discuss the temperature and thermal stability relative to the horizon radius for different values of model parameters $$\beta _0$$

β
0

and $$\alpha _2$$

α
2

. Moreover, we analyze thermodynamical geometries like Ruppeiner, Weinhold and Hendi Panahiyah Eslam Momennia formulation and calculate respective scalar curvatures for BTZ-like black holes. Interestingly, the black holes have no singularity in some cases, i.e., completely regular. We obtain the inversion temperatures and inversion curves and investigate the similarities and differences between van der Waals and charged fluids. To discuss this expansion, we use a BTZ-like black hole. Further, we establish the position of the inversion point versus different values of mass $$\mu $$
μ
, and the parameters $$\beta _0$$

β
0

and $$\alpha _2$$

α
2

for such a BTZ-like black hole. The Joule–Thomson coefficient $$\mu $$
μ
at this point disappears. A crucial trait upon which we relied to inspect the sign of quantity $$\mu $$
μ
to get the cooling-heating areas. We also investigate the energy emission depending upon the frequency $$\omega $$
ω
.
Back-to-back azimuthal correlations in $$\mathrm {Z} +$$jet events at high transverse momentum in the TMD parton branching method at next-to-leading order
Authors: Yang H. , Bermudez Martinez A. , Estevez Banos L. I. , Hautmann F. , Jung H. , Mendizabal M. , Moral Figueroa K. , Prestel S. , Taheri Monfared S. , van Kampen A. M. , Wang Q. , Wichmann K.
Published: August 2022

Azimuthal correlations in $$\mathrm {Z} +$$

Z
+

jet production at large transverse momenta are computed by matching Parton-Branching (PB) TMD parton distributions and showers with NLO calculations via MCatNLO. The predictions are compared with those for dijet production in the same kinematic range. The azimuthal correlations $$\Delta \phi $$

Δ
ϕ

between the Z boson and the leading jet are steeper compared to those in dijet production at transverse momenta $$\mathcal{O}(100)$$

O
(
100
)

 GeV , while they become similar for very high transverse momenta $${{\mathcal {O}}}(1000)$$

O
(
1000
)

 GeV . The different patterns of $$\mathrm {Z} +$$

Z
+

jet and dijet azimuthal correlations can be used to search for potential factorization-breaking effects in the back-to-back region, which depend on the different color and spin structure of the final states and their interferences with the initial states. In order to investigate these effects experimentally, we propose to measure the ratio of the distributions in $$\Delta \phi $$

Δ
ϕ

for $$\mathrm {Z} +$$

Z
+

jet- and multijet production at low and at high transverse momenta, and compare the results to predictions obtained assuming factorization. We examine the role of theoretical uncertainties by performing variations of the factorization scale, renormalization scale and matching scale. In particular, we present a comparative study of matching scale uncertainties in the cases of PB-TMD and collinear parton showers.
Non-Gaussianity and secondary gravitational waves from primordial black holes production in $$\alpha $$-attractor inflation
Authors: Rezazadeh Kazem , Teimoori Zeinab , Karimi Saeid , Karami Kayoomars
Published: August 2022

We study the non-Gaussianity and secondary Gravitational Waves (GWs) in the process of the Primordial Black Holes (PBHs) production from inflation. In our work, we focus on the $$\alpha $$
α
-attractor inflation model in which a tiny bump in the inflaton potential enhances the amplitude of the curvature perturbations at some scales and consequently leads to the PBHs production with different mass scales. We implement the computational code BINGO which calculates the non-Gaussianity parameter in different triangle configurations. Our examination implies that in this setup, the non-Gaussianity gets amplified significantly in the equilateral shape around the scales in which the power spectrum of the scalar perturbations undergoes a sharp declination. The imprints of these non-Gaussianities can be probed in the scales corresponding to the BBN and $$\mu $$
μ
-distortion events, or in smaller scales, and detection of such signatures in the future observations may confirm the idea of our model for the generation of PBHs or rule it out. Moreover, we investigate the secondary GWs in this framework and show that in our model, the peak of the present fractional energy density is obtained as $$\Omega _{\mathrm{GW0}} \sim 10^{-8}$$

Ω

GW
0

∼

10

-
8

at different frequencies which depends on the model parameters. These results lie well within the sensitivity region of some GWs detectors at some frequencies, and therefore the observational compatibility of our model can be evaluated by the forthcoming data from these detectors. We further provide some estimations for the tilts of the induced GWs spectrum in the different intervals of frequency, and demonstrate that the spectrum obeys the power-law relation $$\Omega _{\mathrm{GW0}}\sim f^{n}$$

Ω

GW
0

∼

f
n

in those frequency bands.
Soil Moisture Retrieval Using Sail Squirrel Search Optimization-based Deep Convolutional Neural Network with Sentinel-1 Images
Authors: Preetham Anusha , Battu Vishnu Vardhan
Published: August 2022

Soil Moisture (SM) is an environmental descriptor, which acts as the affiliation between the atmosphere and the earth’s surface. Various SM retrieval methods are developed to abolish the influence of vegetation cover attenuation, surface roughness, and scattering to find an association among SM and backscatter coefficient. To understand the relationship between various vegetation parameters and backscatter coefficient poses a great challenge in SM retrieval. Hence, an efficacious SM retrieval method is afforded using the proposed Sail Squirrel Search Optimization-based Deep Convolutional Neural Network (SSSO-based Deep CNN). Here, the proposed SSSO is derived by concatenating the Sail Fish Optimization (SFO) with Squirrel Search Algorithm (SSA). The Deep CNN performs the process of SM retrieval using vegetation indices. The fitness measure of the proposed optimization enables to find the best solution to update the weights of the classifier for increasing the efficiency of the retrieval mechanism. By training Deep CNN with the proposed optimization, the soil moisture of an area is effectively retrieved. However, the proposed SSSO-based Deep CNN obtained minimal estimation error and minimal RMSE of 0.550 and 0.726 using sentinel-1 data, respectively.
Following Ussing’s legacy: from amphibian models to mammalian kidney and brain
Authors: Blazer-Yost Bonnie L.
Published: August 2022

Professor Hans H. Ussing (1911–2000) was one of the founding members of the field of epithelial cell biology. He is most famous for the electrophysiological technique that he developed to measure electrogenic ion flux across epithelial tissues. Ussing-style electrophysiology has been applied to multiple tissues and has informed fields as diverse as amphibian biology and medicine. In the latter, this technique has contributed to a basic understanding of maladies such as hypertension, polycystic kidney disease, cystic fibrosis, and diarrheal diseases to mention but a few. In addition to this valuable contribution to biological methods, Prof. Ussing also provided strong evidence for the concept of active transport several years before the elucidation of Na+K+ATPase. In addition, he provided cell biologists with the important concept of polarized epithelia with specific and different transporters found in the apical and basolateral membranes, thus providing these cells with the ability to conduct directional, active and passive transepithelial transport. My studies have used Ussing chamber electrophysiology to study the toad urinary bladder, an amphibian cell line, renal cell lines, and, most recently, choroid plexus cell lines. This technique has formed the basis of our in vitro mechanistic studies that are used in an iterative manner with animal models to better understand disease progress and treatment. I was honored to be invited to deliver the 2022 Hans Ussing Lecture sponsored by the Epithelial Transport Group of the American Physiological Society. This manuscript is a version of the material presented in that lecture.
Regional skin wetness perception and its modulation by warm and cold whole body skin temperatures in people with multiple sclerosis
Authors: Christogianni Aikaterini , Bibb Richard , Filtness Ashleigh , Filingeri Davide
Published: August 2022

Skin wetness sensing is important for thermal stress resilience. Individuals with multiple sclerosis (MS) present greater vulnerability to thermal stress; yet, it is unclear whether they present wetness-sensing abnormalities. We investigated the effects of MS on wetness sensing and their modulation with changes in mean skin temperature (Tsk). Twelve participants with MS [5 males (M)/7 females (F); 48.3 ± 10.8 yr; Expanded Disability Status Scale (EDSS) range: 1–7] and 11 healthy controls (4 M/7 F; 47.5 ± 11.3 yr) undertook three trials, during which they performed a quantitative sensory test with either a thermoneutral (30.9°C), warm (34.8°C), or cold (26.5°C) mean Tsk. Participants reported on visual analog scales local wetness perceptions arising from the static and dynamic application of a cold-, neutral-, and warm-wet probe (1.32 cm2; water content: 0.8 mL), to the index finger pad, forearm, and forehead. Data were analyzed for the group-level effect of MS, as well as for its individual variability. Our results indicated that MS did not alter skin wetness sensitivity at a group level, across the skin sites and temperature tested, neither under normothermia nor under conditions of shifted thermal state. However, when taking an individualized approach to profiling wetness-sensing abnormalities in MS, we found that 3 of the 12 participants with MS (i.e., 25% of the sample) presented a reduced wetness sensitivity on multiple skin sites and to different wet stimuli (i.e., cold, neutral, and warm wet). We conclude that some individuals with MS may possess reduced wetness sensitivity; however, this sensory symptom may vary greatly at an individual level. Larger-scale studies are warranted to characterize the mechanisms underlying such individual variability.
Research on the Optimization of International News Communication Teaching Mode Based on Cluster Analysis under the Background of Big Data
Authors: Cheng Yu , Zhou Zongyi
Published: August 2022

With the rapid development of the Internet, the new and advanced teaching mode is becoming increasingly popular, which has become a focus in the field of education in recent years. Therefore, the research and development of online teaching platforms have become a hot topic. This article takes international news communication as the research background, aiming at the unclear existing level and weak target. The teaching mode of the single teaching form puts forward the “embedded online intelligent teaching platform.” A set of online innovative teaching platforms for large embedded systems is studied and established. The improved clustering analysis uses the GBKM clustering analysis algorithm to integrate grid clustering and K-means clustering and introduces the new meshing algorithm and the new function to calculate the density threshold. Through theoretical analysis and experimental verification, the superiority of the GBKM algorithm is demonstrated. As the GBKM clustering analysis algorithm is the core algorithm of a personalized smart learning system, it is applied to an online intelligent teaching platform. The experimental results show that the teaching effect of the optimized teaching mode is significantly improved.
Adaptive Biological Neural Network Control and Virtual Realization for Engineering Manipulator
Authors: Guo Hao , Liu Hongyang , Zhou Dashuai , He Yao
Published: August 2022

By analyzing the feasibility of the digital twin technology in the assembly of construction machinery, the assembly process of the construction manipulator in the engineering environment is discussed. According to the application criteria and modeling requirements of digital twin, the overall framework of digital twin engineering manipulator assembly modeling and simulation is constructed from three aspects: model layer, data layer, and application layer. According to the operation task characteristics of space engineering manipulator, the feasibility of the control method based on joint angular velocity is analyzed, and the task environment of space engineering manipulator based on Markov model is defined. Aiming at the application of the algorithm in the control task of the space engineering manipulator, a reward function with the addition of the angular velocity soft bound term is designed, which improves the strategy optimization process of the algorithm and obtains a better control effect of the engineering manipulator. The motion trajectory of the end of the engineering manipulator is directly given on the simulation platform, and the expected motion of each joint of the engineering manipulator is calculated through the kinematics of the engineering manipulator. It can be seen from the simulation results that the controllers designed in this study can achieve ideal control effects. With the help of Baxter robot platform, the control algorithm designed in this study is applied to the actual engineering manipulator control, and the effectiveness of the control algorithm is further proved by the actual control effect.
Transfer Learning-Based Vehicle Collision Prediction
Authors: Yang Li , Wang Zonggao , Ma Lijun , Dai Wei
Published: August 2022

Traffic accident is an important problem in modern society. Vehicle collision prediction is one of the key technical points that must be broken through in the future driving system. However, due to the complexity of traffic environment and the difference of emergency ability of drivers, it is very difficult to predict vehicle collision. Although experts and scholars have tried to monitor and predict accidents in real time according to environmental conditions, overly agile warning or inaccurate prediction may cause serious consequences. Therefore, in order to more accurately predict the occurrence of vehicle collision, this paper analyses and models the driving mode of the vehicle based on transfer learning and using the previous performance data of the vehicle, so as to predict the future collision situation and even the collision time of the vehicle. Finally, using a real-world Internet of Vehicles data set, this paper implements a large number of experiments to verify the effectiveness of the proposed model.
Analysis on Effects of Laparoscopic Total Hysterectomy Combined with High Hysterosacral Ligament Suspension in the Treatment for Uterine Prolapse
Authors: Qiu Bing , Wang Aili , Chen Yanxin , Wang Zhijun
Published: August 2022

Objective. To investigate the effect of laparoscopic total hysterectomy combined with high hysterosacral ligament suspension on the treatment for uterine prolapse. Methods. A total of 100 patients with uterine prolapse treated in our two hospitals from January 2019 to December 2021 were included in this study, which were divided into two groups through the number double-blind method, with 50 patients in each group. The control group was treated with transvaginal total hysterectomy, and the research group was treated with laparoscopic total hysterectomy combined with high hysterosacral ligament suspension. The surgical effect on patients was evaluated through the pelvic organ prolapse quantification method (POP-Q). The patients’ adverse reactions were compared. The quality of sexual life was evaluated with the short form of the pelvic organ prolapse/urinary incontinence sexual questionnaire (PISQ-31). And, the patients’ quality of life was also evaluated with the pelvic floor distress inventory-short form 20 (PFDl-20). Results. The surgical effect on the control group and the research group was 80.00% and 96.00%, respectively, with statistical significance (X2 = 6.601,

P
<
0.001

). The incidences of adverse reactions of the two groups were 4.00% and 6.00%, respectively, which were comparable (X2 = 0.211,

P
=
 
0.646

). The total PISQ-31 scores before surgery of the control group (97.07 ± 9.80) and the research group (97.02 ± 9.80) were comparable (t = 0.020,

P
=
0.984

), and those after surgery were 112.55 ± 13.78 in the control group and 130.80 ± 17.42 in the research group, respectively, with statistical significance (t = 4.500,

P
<
0.001

). And, the total PFDl-20 scores before surgery of the control group (72.50 ± 13.58) and the research group (72.50 ± 13.60) were comparable (t = 0.057,

P
=
0.098

), and that after surgery were 56.10 ± 10.51 in the control group and 42.30 ± 8.05 in the research group, respectively, with statistical significance (t = 5.709,

P
<
0.001

). Conclusion. Laparoscopic total hysterectomy combined with high hysterosacral ligament suspension has an ideal effect in patients with uterine prolapse, with few adverse reactions, effectively promoting the improvement of the quality of patients’ sexual life and their life. This combination has the significance of active promotion in the clinic.
Analysis and Evaluation of Sports Effect Based on Random Forest Algorithm under Big Data
Authors: Liang Kai , Zang Dongdong
Published: August 2022

Under the background of big data, all walks of life have carried out in-depth informatization construction. As an important part of national education, the informatization construction of universities cannot be ignored. In recent years, the state has promulgated various policies and regulations, which provide a guarantee for the normal development of school physical education and can improve the current situation of college students’ declining physical health to a certain extent. This study attempts to set some specific indicators to promote the better implementation of college sports so that it can supervise and inspect them from all aspects during the actual development of college sports and provide a standard for measuring the implementation effect of college sports. Based on the RF (Random Forest) algorithm, this paper puts forward an evaluation algorithm of students’ sports achievements, which can be used to predict students’ sports achievements and at the same time, find out the factors that affect students’ learning and rank them in importance. The results show that the confidence level of sports effect evaluation by this method is high, and the average confidence level is above 0.96. Conclusion. This method has improved the effect of sports effect evaluation, thus effectively guiding sports skills training and improving sports skills.
Enhancing Online Epidemic Supervising System by Compartmental and GRU Fusion Model
Authors: Ma Junyi , Wang Xuanliang , Wang Yasha , Wang Jiangtao , Chu Xu , Zhao Junfeng
Published: August 2022

The global pandemic, COVID-19, is an acute respiratory infectious disease caused by the 2019 novel coronavirus. Building the online epidemic supervising system to provide COVID-19 dynamic prediction and analysis has attracted the attention of the industry and applications community. In previous studies, the compartmental models and deep neural networks (DNNs) played important roles in predicting and analyzing the dynamics of the pandemic. Nevertheless, the compartmental model has limited ability to fit historical data and thus leads to unsatisfactory prediction accuracy due to the difficulty in parameter estimation. For DNNs, the lack of interpretability makes it difficult to explain the prediction results; thus, it cannot provide an in-depth understanding of the transmission mechanism of the pandemic. We propose a fusion model to leverage the merits of both models and resolve their shortcomings. The fusion model extracts epidemic-related knowledge from the state-of-the-art SEIDR compartmental model to guide the training of the GRU model, which can preserve the interpretability and achieve a good performance in predicting epidemic dynamics. This model can help to enhance the online epidemic supervising system by providing more accurate prediction results and deeper analysis. Our extensive experiments across multiple epidemic datasets from six European countries demonstrate that our model outperforms existing state-of-the-art baselines in predicting the active confirmed cases. More importantly, by analyzing the effective reproductive number, our method can reveal the risk of the second wave of the epidemic in Europe and justify the importance of social distancing to control the outbreak of the epidemic.
Glycyrrhizic Acid Protects Experimental Sepsis Rats against Acute Lung Injury and Inflammation
Authors: Shen Jiali , Hua Zhongjie , Chai Yueyang
Published: August 2022

Background. The incidence of acute lung injury/acute respiratory distress (ALI/ARDS) is high in sepsis aggravating morbidity and mortality. Glycyrrhizic acid (GA) has pharmacological activities in the treatment of inflammation and antiviral. Materials and Methods. Sepsis rats were constructed by the cecal ligation and puncture (CLP) surgery. After GA (25 and 50 mg/kg) injection, the survival rate, blood oxygen, biochemical indexes, myeloperoxidase (MPO) activity, and wet/dry weight ratio of the lung were observed. The bronchoalveolar lavage fluid was collected to count the cells and measure the level of TNF-α, IL-1β, IL-10, and high mobility group box-1 protein (HMGB1). Lung tissue sections were taken to observe the levels of histopathological injury and apoptosis by HE and TUNEL staining. The levels of HMGB1, TLR4, p-38 MAPK, NF-κB, and ERK1/2 proteins were observed by immunohistochemistry and Western blot. Results. GA treatment improved the survival rate, blood oxygen, ALT, AST, BUN, and Scr of CLP rats. It could advance the MPO activity, the wet/dry weight ratio, histopathological injury, apoptosis, and the IL-10 level in the lung. After GA injection, the number of total cells, neutrophils, and macrophages in the CLP rats was reduced and the levels of TNF-α, IL-1β, HMGB1, TLR4, p-38 MAPK, and ERK1/2 in the CLP rat were also repressed. Conclusions. GA treatment may improve the sepsis-induced ALI/ARDS and inflammation by inhibiting HMBG1. This study provided an experimental basis for the prevention and treatment of ALI/ARDS caused by sepsis.
Intelligent Garment Graphic Design System for Artificial Intelligence and 3D Image Analysis
Authors: Yang Jia , Xia Wei , Hong Ying
Published: August 2022

The application of artificial intelligence technology not only realizes the transformation of people’s production and lifestyle but also promotes the transformation of the clothing industry. At present, the proportion of artificial intelligence in the clothing industry is becoming larger and larger. Using its advanced methods and technologies of information display and data analysis, this study is worth transforming and optimizing the clothing graphic design system under the artificial intelligence environment, so as to promote the intelligent development of the clothing industry. This study proposes to use three-dimensional image analysis algorithm technology to guide the integration of garment graphic design process and computer network, which is helpful to analyze and solve the imbalance between the singularity of traditional garment pattern design and people’s aesthetic appreciation at present. Using the technology and principle of artificial intelligence for reference, the intelligent clothing graphic design system is constructed and analyzed. In the analysis of the clothing graphic design system of a group, the group applied artificial intelligence technology to the customized clothing in the production workshop of the company. Although the efficiency of the adjustment system is not as good as that of mass production, it eliminates inventory, and its net profit can reach 20–30%, which is much higher than that of traditional clothing. In contrast, the industry reported a net profit of 12% of the highest level. Therefore, it is urgent to analyze the intelligent garment graphic design system through artificial intelligence technology.
Improvement in Disability Mediates the Effect of Self-Efficacy on Pain Relief in Chronic Low Back Pain Patients with Exercise Therapy
Authors: Shinohara Yuta , Wakaizumi Kenta , Ishikawa Aiko , Ito Mari , Hoshino Reiko , Tanaka Chisato , Takaoka Saki , Kawakami Michiyuki , Tsuji Osahiko , Fujisawa Daisuke , Fujiwara Toshiyuki , Tsuji Tetsuya , Morisaki Hiroshi , Kosugi Shizuko
Published: August 2022

Background. The biopsychosocial mechanism by which exercise leads to improvement in chronic low back pain (CLBP) remains unstudied. This prospective cohort study was performed to examine the effectiveness of exercise on pain, disability, and psychological status for CLBP. We also tested path analytic models in which changes in these variables were included. Methods. CLBP patients who visited the Interdisciplinary Pain Center of Keio University Hospital from July 2018 to April 2020 were included. The propensity score matching was performed between patients who underwent exercise (the exercise group) and those who did not (the control group). At the first visit and at the 3-month follow-up, pain (Numerical Rating Scale (NRS)), disability (Pain Disability Assessment Scale (PDAS)), and psychological status (Pain Self-Efficacy Questionnaire (PSEQ), and Pain Catastrophizing Scale (PCS)) were assessed. Changes in pain and disability at the follow-up were compared between the groups. The relationships between changes in pain, disability, and psychological variables were examined using Pearson’s correlation and mediation analysis. Results. A significantly larger decrease in the PDAS was observed in the exercise group (N = 49) than in the control (N = 49) (

p
<
0.05

). Increased PSEQ scores were significantly correlated with decreased NRS scores in both groups. In the exercise group, decreased PDAS fully mediated the relationship between increased PSEQ and decreased NRS (

P
<
0.05

). Conclusion. Exercise improved disability, and the improved disability by exercise mediated the effect of increased self-efficacy on pain relief in CLBP patients.
Optimization of Tennis Teaching Resources and Data Visualization Based on Support Vector Machine
Authors: Zhang Shaokun , Yu Huan
Published: August 2022

In recent years, with the continuous development of machine learning technology, this technology has achieved success in many fields and activities. Therefore, using machine learning technology for fuzzy research has a good research prospect. In the development of related research, the author of this study noticed that some researchers began to use tennis machine learning technology and achieved good results. However, most of the research is only for simple analysis and is related to the current work. It cannot be used to move a solid tennis ball, nor it can make small changes to the original tennis movement; thus, it cannot carry out a complete and brand-new movement. The defense of tennis first establishes visual teaching tools with the help of various courses and visual teaching techniques to improve the teaching effect. By optimizing the network data, this study constructs the corresponding data search model, which downloads a large amount of data from the network ram, so as to separate the impact of the network environment on the load. The simulation results show that the model is optimized for the high-quality 3G network environment, and the load time and energy consumption are greatly reduced. It is more efficient in WiFi and a a high-quality 4G network environment.
Segmentation for Human Motion Injury Ultrasound Medical Images Using Deep Feature Fusion
Authors: Sun Jingmeng , Liu Yifei
Published: August 2022

Image processing technology assists physicians in the analysis of athletes’ human motion injuries, not only to improve the accuracy of athletes’ injury detection but also to improve the localization and recognition of injury locations. It is important to accurately segment human motion injury ultrasound medical images. To address many problems such as poor effect of traditional ultrasonic medical image segmentation algorithm for a sports injury. Therefore, we propose a segmentation algorithm for human motion injury ultrasound medical images using deep feature fusion. First, the accurate estimated value of human posture is extracted and combined with image texture features and image gray value as the target feature value of the ultrasonic medical image of human motion injury. Second, the image features are deeply fused by an adaptive fusion algorithm to enhance the image resolution. Finally, the best segmentation value of the image is obtained by the trained support vector machine to realize the accurate segmentation of human motion injury ultrasonic medical image. The results show that the average accuracy of the posture accurate estimation of the proposed algorithm is 95.97%; the segmentation time of the human motion injury ultrasound medical image of the proposed algorithm is below 150 ms; and the convergence of the algorithm is completed when the number of iterations is 3. The maximum segmentation error rate is 2.68%. The image segmentation effect is consistent with the ideal target segmentation effect. The proposed algorithm has important application value in the field of ultrasonic medical diagnosis of sports injury.