“Lifting More” is Associated with Lower Risks of Depression in University Students

Research on the population in western world showed that,MSE(muscle-strengthening exercise)is beneficial to the treatment of mental disorders.However,the situation in Chinese adults is little known.For this reason,the study is performed to understand the connection between depression and MSE among college and university students in China aged between 18 to 24.1793 college students have been recruited,and their average age is 20.67.A questionnaire has been developed and it is self-reported and designed to collect information about MSE and participants,including body mass index and sex and so on.Sleep and physical activity have been evaluated by introducing two scales,that is,Pittsburgh Sleep Quality Index and International Physical Activity Questionnaire-Short Form,respectively.Moreover,Patient Health Questionnaire-9 has been adopted to indicate the severity of depression of participants.The link between depression and MSE has been studied by introducing multilevel linear regression.Among all these study participants,just 24.87%of them met the MSE standards of World Health Organization,that is,more than 2 days every week.The average depression score was 6.80(±5.19).Greater num-ber of days for MSE shows negative association with the depression,with (beta=-0.17,95%CI:-0.31 to-0.03,p=0.015).Those students failing to meet MSE standards are more susceptible to the depression risk(beta=0.63,95%CI:0.09–0.19,p=0.027).The results show that,there is a relationship between MSE participation and relieved status of depression among young adults in China.Interventions designed to relieve depression can be developed on the basis of MSE.

An Intelligent Sensor Data Preprocessing Method for OCT Fundus Image Watermarking Using an RCNN

Watermarks can provide reliable and secure copyright protection for optical coherence tomography(OCT)fundus images.The effective image segmentation is helpful for promoting OCT image watermarking.However,OCT images have a large amount of low-quality data,which seriously affects the performance of segmentationmethods.Therefore,this paper proposes an effective segmentation method for OCT fundus image watermarking using a rough convolutional neural network(RCNN).First,the rough-set-based feature discretization module is designed to preprocess the input data.Second,a dual attention mechanism for feature channels and spatial regions in the CNN is added to enable the model to adaptively select important information for fusion.Finally,the refinement module for enhancing the extraction power of multi-scale information is added to improve the edge accuracy in segmentation.RCNN is compared with CE-Net and MultiResUNet on 83 gold standard 3D retinal OCT data samples.The average dice similarly coefficient(DSC)obtained by RCNN is 6%higher than that of CE-Net.The average 95 percent Hausdorff distance(95HD)and average symmetric surface distance(ASD)obtained by RCNN are 32.4%and 33.3%lower than those of MultiResUNet,respectively.We also evaluate the effect of feature discretization,as well as analyze the initial learning rate of RCNN and conduct ablation experiments with the four different models.The experimental results indicate that our method can improve the segmentation accuracy of OCT fundus images,providing strong support for its application in medical image watermarking.

Research on the Current Situation and Cultivation of English Learning Motivation of Non-English Majors

English is not only a language,but also a tool.There are many factors that affect students’English learning quality and ef?ficiency,among which the biggest one is learning motivation.At present,some non-English majors in our country lack the motiva?tion of English learning,and the phenomenon of students’grade differentiation becomes more and more obvious,which seriously affects the process and quality of English teaching in the whole class.Analyzing and exploring the present situation and cultivation of English learning motivation of non-English majors can improve the quality and level of English learning of Chinese college stu?dents as a whole.

A Multi-Watermarking Algorithm for Medical Images Using Inception V3 and DCT

Medical images are a critical component of the diagnostic process for clinicians.Although the quality of medical photographs is essential to the accuracy of a physician’s diagnosis,they must be encrypted due to the characteristics of digital storage and information leakage associated with medical images.Traditional watermark embedding algorithm embeds the watermark information into the medical image,which reduces the quality of the medical image and affects the physicians’judgment of patient diagnosis.In addition,watermarks in this method have weak robustness under high-intensity geometric attacks when the medical image is attacked and the watermarks are destroyed.This paper proposes a novel watermarking algorithm using the convolutional neural networks(CNN)Inception V3 and the discrete cosine transform(DCT)to address above mentioned problems.First,the medical image is input into the Inception V3 network,which has been structured by adjusting parameters,such as the size of the convolution kernels and the typical architecture of the convolution modules.Second,the coefficients extracted from the fully connected layer of the network are transformed by DCT to obtain the feature vector of the medical image.At last,the watermarks are encrypted using the logistic map system and hash function,and the keys are stored by a third party.The encrypted watermarks and the original image features are performed logical operations to realize the embedding of zero-watermark.In the experimental section,multiple watermarking schemes using three different types of watermarks were implemented to verify the effectiveness of the three proposed algorithms.Our NC values for all the images are more than 90%accurate which shows the robustness of the algorithm.Extensive experimental results demonstrate the robustness under both conventional and high-intensity geometric attacks of the proposed algorithm.

Waste to treasure:Reutilization of fluid catalytic cracking coke block as photothermal conversion material for water evaporation

This study aims to analyze the coking process and propose an effective method for the reutilization of fluid catalytic cracking(FCC)coke block.Herein,we analyzed the basic characteristics and chemical composition of FCC coke blocks.The results showed that the main components were carbon,oxygen,and aluminum,accounting for 60.8%,26.6%,and 11.5%,respectively.Under the conventional catalytic cracking reaction temperature from 500°C to 600°C,the formation of the first aromatic hydrocarbon was particularly important for the formation of coke.The condensation of oil-gas-entrained catalyst particles and their heavy components was the physical cause of coking,while the dehydrogenation condensation reaction of oil-gas heavy components was the chemical factor.In addition,the membrane prepared by powdered coke had excellent photothermal conversion ability,which could be heated to more than 110°C within 360 s under two fixed light intensities.The evaporation rate of photothermal water was 5.89 kg m 2 h−1,which has great industrial application potential.These works provide a novel and effective method of separation membrane for the reutilization of FCC coke blocks.

A Novel Robust Watermarking Algorithm for Encrypted Medical Image Based on DTCWT-DCT and Chaotic Map

In order to solve the problem of patient information security protection in medical images,whilst also taking into consideration the unchangeable particularity of medical images to the lesion area and the need for medical images themselves to be protected,a novel robust watermarking algorithm for encrypted medical images based on dual-tree complex wavelet transform and discrete cosine transform(DTCWT-DCT)and chaotic map is proposed in this paper.First,DTCWT-DCT transformation was performed on medical images,and dot product was per-formed in relation to the transformation matrix and logistic map.Inverse transformation was undertaken to obtain encrypted medical images.Then,in the low-frequency part of the DTCWT-DCT transformation coefficient of the encrypted medical image,a set of 32 bits visual feature vectors that can effectively resist geometric attacks are found to be the feature vector of the encrypted medical image by using perceptual hashing.After that,different logistic initial values and growth parameters were set to encrypt the watermark,and zero-watermark technology was used to embed and extract the encrypted medical images by combining cryptography and third-party concepts.The proposed watermarking algorithm does not change the region of interest of medical images thus it does not affect the judgment of doctors.Additionally,the security of the algorithm is enhanced by using chaotic mapping,which is sensitive to the initial value in order to encrypt the medical image and the watermark.The simulation results show that the pro-posed algorithm has good homomorphism,which can not only protect the original medical image and the watermark information,but can also embed and extract the watermark directly in the encrypted image,eliminating the potential risk of decrypting the embedded watermark and extracting watermark.Compared with the recent related research,the proposed algorithm solves the contradiction between robustness and invisibility of the watermarking algorithm for encrypted medical images,and it has good results against both conventional attacks and geometric attacks.Under geometric attacks in particular,the proposed algorithm performs much better than existing algorithms.

Meeting 24-h Movement Guidelines is Related to Better Academic Achievement:Findings from the YRBS 2019 Cycle

This research is designed to investigate the relationship between the 24-h movement guidelines(24-HMG)and self-reported academic achievement(AA)using nationally representative data derived from the 2019 U.S.National Youth Risk Behaviour Survey.A multiple-stage cluster sampling procedure has been adopted to ensure a representative sample(N=9127 adolescents;mean age=15.7 years old;male%=49.8%).Logistic regression has been adopted to obtain the odds ratio(OR)regarding the associations between adherence to 24-HMG and AA while controlling for ethnicity,body mass index,sex and age.The prevalence of meeting the 24-h movement guidelines in isolation and combination varied greatly(physical activity=23.3%,screen time=32.5%,sleep=22.3%,and 24-HMG=2.8%),while the percentage of highest-class AA was 42.5%.Compared with the situation when none of 24-HMG is met,the achievement of any of the combined guidelines(except for meeting the physical activity guidelines)was significantly associated with higher odds of achieving first-class AA.Meeting the sleep guideline had 1.42 times increased likelihood to achieve highest-class AA as compared with not meeting the sleep guideline.Meeting screen time guidelines and physical activity guidelines,respectively,were 1.32 and 1.13 times more likely to report first-class AA;but meeting the guidelines of physical activity was not significantly related to AA.Meeting the 24-HMG had the highest odds of achieving first-class AA(OR=2.01,95%CI:1.47-2.73).In both sexes,adolescents who met 24-HMG self-reported better AA(boys OR=2.05,95%CI:1.34-3.15;girls OR=2.26,95%CI:1.36-3.76).Significant relationships were observed in adolescents from 9-10th grade,but not higher grades.Our research findings suggest that optimal movement behaviours can be seen as an important element to better academic achievement among U.S.adolescents.Future studies can adopt our discoveries to promote adolescents’academic achievement through implementing optimal 24-h movement behaviour patterns.