基于VMD-Self-attention-LSTM的水闸深基坑变形智能预测方法

针对水闸深基坑变形监测数据具有非稳定性的特点,提出了基于VMD-Self-attention-LSTM的水闸深基坑变形预测方法。该方法分为三个主要模块,第一模块采用VMD算法自适应调整变形分解模数,将原始变形数据分解为若干具有明显周期规律的分量,有效解决变形数据的非稳定性,为提升变形预测精度奠定初步基础;第二模块,在传统LSTM算法基础上进一步发展Self-attention-LSTM方法,提升模型对基坑变形样本的时序关系挖掘能力,进而提升基坑变形预测精度;第三模块,将各分量对应的变形预测结果重构得到最终预测值。实例分析可知,所提方法有效解决了非稳定特性给变形预测精度带来的不利影响,与VMD-LSTM、Self-attention-LSTM、LSTM等深度学习方法相比,VMD-Self-attention-LSTM的预测精度最大提升41.49%,与BP、ELM等传统机器学习算法相比,预测精度最大提升50.43%,为水闸深基坑安全监控模型的构建提供了新思路。

Countermeasures on Elevating Self-development Ability of Western Rural Areas on the Basis of Visual Threshold of Human Capital

The thesis underscores the significant role of human capital in elevating rural self-development ability.The investment of human capital is making great contribution to western rural economic growth,and it plays an irreplaceable role in promoting the socio-economic development prominently.The characteristics of human resources development in western rural areas for the moment are analyzed as follows:the employment structure is simple;the structure of human resources is irrational;the shortage of human resources aggregate is accompanied by the critical phenomenon of idling;the human resources drain badly;there is low input and low output of talents.The thesis also propounds the fundamental approaches in order to elevating self-development ability of western rural areas:we should practise and implement National Medium and Long-term Planning of Talents Development and lay down as soon as possible the detailed matching policies and measures based on the reality of western rural areas;we should develop the education in western rural areas vigorously,and formulate the developmental strategies of bridging "knowledge gap";we should establish sound rural human resources development system,and reinforce the degree of training returning-home migrant workers;we should adjust the structure of human investment timely as needed,and strengthen the degree of human capital investment conspicuously.

Survey and Inquiry for English Teachers' Self-development in Freshly Certificated Universities in Guangxi

With great a quantity of upgraded colleges accounted in Guangxi Zhuang Automous Region,self-development of college English teachers directly appeals to the quality of teaching,English curriculum program and academic development competence.The paper preliminarily stepped in the internal and external elements on the them to carry on inquiry to the findings that these teachers are in good consciousness of language skills,specialized knowledge and personal communication,common improvement conformed with the achievements abroad.And by interviewing the convenient sampling the inquiryshows that they have the consciousness of changing with times.The hardest challenging is deficiency of quality education channels to promote their teaching competence and academic ability.The paper is advisory for playing vital role in their self-development inquiry in a whole perspective.

Inquiry Summary for Universities English Teachers' Self-development

College English teachers' Self development apparently affects teaching quality, major curriculum development and inquiry development. The scope of inquiry summary indicates the intrinsic factors as awareness of professional competence, professional-knowledge learning awareness, awareness of teaching means, development awareness and the notions alike. And the summary comes to the findings that foreign ones ignore the effect of the external factors, the domestic just focus on the teaching pattern of team, yet ignore the scientific and inquiry group construction.

基于Self-Attention-BiLSTM网络的西瓜种苗叶片氮磷钾含量高光谱检测方法

元素含量无损检测技术可以为植物生长发育的环境精准调控提供关键实时数据。以西瓜苗为例,提出了一种基于图谱特征融合的氮磷钾含量深度学习检测方法。首先,使用高光谱仪拍摄西瓜苗叶片的高光谱图像,使用连续流动化学分析仪测定叶片的3种元素含量。然后,采用基线偏移校正(BOC)叠加高斯平滑滤波(GF)的光谱预处理方法和随机森林算法(RF)建立预测模型,基于竞争性自适应重加权采样(CARS)和连续投影算法(SPA)2种算法初步筛选出特征波长,再综合考虑波长数和建模精度设计了一种最优波长评价方法,将波长数进一步减少到3~4个。最后,提取使用U-Net网络分割的彩色图像颜色和纹理特征,和光谱反射率特征一起作为输入,基于自注意力机制-双向长短时记忆(Self-Attention-BiLSTM)网络构建了3种元素含量的预测模型。实验结果表明,氮磷钾含量预测的R2分别为0.961、0.954、0.958,RMSE分别为0.294%、0.262%、0.196%,实现了很好的建模效果。使用该模型对另2个品种西瓜进行测试,R2超过0.899、RMSE小于0.498%,表明该模型具有很好的泛化性。该高光谱建模方法使用少量波长光谱即实现了高精度检测,在精度和效率上达成了很好的平衡,为后续便携式高光谱检测装备开发奠定了理论基础。

Self-Healable and Stretchable PAAc/XG/Bi_(2)Se_(0.3)Te_(2.7) Hybrid Hydrogel Thermoelectric Materials

Thermoelectric power generators have attracted increasing interest in recent years owing to their great potential in wearable electronics power supply.It is noted that thermoelectric power generators are easy to damage in the dynamic service process,resulting in the formation of microcracks and performance degradation.Herein,we prepare a new hybrid hydrogel thermoelectric material PAAc/XG/Bi_(2)Se_(0.3)Te_(2.7)by an in situ polymerization method,which shows a high stretchable and self-healable performance,as well as a good thermoelectric performance.For the sample with Bi_(2)Se_(0.3)Te_(2.7)content of 1.5 wt%(i.e.,PAAc/XG/Bi2Se0.3Te27(1.5 wt%)),which has a room temperature Seebeck coefficient of-0.45 mV K^(-1),and exhibits an open-circuit voltage of-17.91 mV and output power of 38.1 nW at a temperature difference of 40 K.After being completely cut off,the hybrid thermoelectric hydrogel automatically recovers its electrical characteristics within a response time of 2.0 s,and the healed hydrogel remains more than 99%of its initial power output.Such stretchable and self-healable hybrid hydrogel thermoelectric materials show promising potential for application in dynamic service conditions,such as wearable electronics.

RepBoTNet-CESA:An Alzheimer’s Disease Computer Aided Diagnosis Method Using Structural Reparameterization BoTNet and Cubic Embedding Self Attention

Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease(AD).Most studies predominantly employ Convolutional Neural Networks(CNNs),which focus solely on local features,thus encountering difficulties in handling global features.In contrast to natural images,Structural Magnetic Resonance Imaging(sMRI)images exhibit a higher number of channel dimensions.However,during the Position Embedding stage ofMulti Head Self Attention(MHSA),the coded information related to the channel dimension is disregarded.To tackle these issues,we propose theRepBoTNet-CESA network,an advanced AD-aided diagnostic model that is capable of learning local and global features simultaneously.It combines the advantages of CNN networks in capturing local information and Transformer networks in integrating global information,reducing computational costs while achieving excellent classification performance.Moreover,it uses the Cubic Embedding Self Attention(CESA)proposed in this paper to incorporate the channel code information,enhancing the classification performance within the Transformer structure.Finally,the RepBoTNet-CESA performs well in various AD-aided diagnosis tasks,with an accuracy of 96.58%,precision of 97.26%,and recall of 96.23%in the AD/NC task;an accuracy of 92.75%,precision of 92.84%,and recall of 93.18%in the EMCI/NC task;and an accuracy of 80.97%,precision of 83.86%,and recall of 80.91%in the AD/EMCI/LMCI/NC task.This demonstrates that RepBoTNet-CESA delivers outstanding outcomes in various AD-aided diagnostic tasks.Furthermore,our study has shown that MHSA exhibits superior performance compared to conventional attention mechanisms in enhancing ResNet performance.Besides,the Deeper RepBoTNet-CESA network fails to make further progress in AD-aided diagnostic tasks.

Unified Description of the Three Stable Particles in Self-Action Allows Determination of Their Relative Masses

The Dirac equation γμ(δμ-eAμ)Ψ=mc2Ψ describes the bound states of the electron under the action of external potentials, Aμ. We assumed that the fundamental form of the Dirac equation γμ(δμ-Sμ)Ψ=0 should describe the stable particles (the electron, the proton and the dark-matter-particle (dmp)) bound to themselves under the action of their own potentials Sμ. The new equation reveals that self energy is consequence of self action, it also reveals that the spin angular momentum is consequence of the dynamic structure of the stable particles. The quantitative results are the determination of their relative masses as well as the determination of the electromagnetic coupling constant.

Safety and efficacy of Kaffes intraductal self-expanding metal stents in the management of post-liver transplant anastomotic strictures

BACKGROUND Endoscopic management is the first-line therapy for post-liver-transplant anas-tomotic strictures.Although the optimal duration of treatment with plastic stents has been reported to be 8-12 months,data on safety and duration for metal stents in this setting is scarce.Due to limited access to endoscopic retrograde cholan-giopancreatography(ERCP)during the coronavirus disease 2019 pandemic in our centre,there was a change in practice towards increased usage and length-of-stay of the Kaffes biliary intraductal self-expanding stent in patients with suitable anatomy.This was mainly due to the theoretical benefit of Kaffes stents allowing for longer indwelling periods compared to the traditional plastic stents.METHODS Adult liver transplant recipients aged 18 years and above who underwent ERCP were retrospectively identified during a 10-year period through a database query.Unplanned admissions post-Kaffes stent insertion were identified manually through electronic and scanned medical records.The main outcome was the incidence of complications when stents were left indwelling for 3 months vs 6 months.Stent efficacy was calculated via rates of stricture recurrence between patients that had stenting courses for≤120 d or>120 d.RESULTS During the study period,a total of 66 ERCPs with Kaffes insertion were performed in 54 patients throughout their stenting course.In 33 ERCPs,the stent was removed or exchanged on a 3-month interval.No pancreatitis,perfor-ations or deaths occurred.Minor post-ERCP complications were similar between the 3-month(abdominal pain and intraductal migration)and 6-month(abdominal pain,septic shower and embedded stent)groups-6.1%vs 9.1%respectively,P=0.40.All strictures resolved at the end of the stenting course,but the stenting course was variable from 3 to 22 months.The recurrence rate for stenting courses lasting for up to 120 d was 71.4%and 21.4%for stenting courses of 121 d or over(P=0.03).There were 28 patients that were treated with a single ERCP with Kaffes,21 with removal after 120 d and 7 within 120 d.There was a significant improvement in stricture recurrence when the Kaffes was removed after 120 d when a single ERCP was used for the entire stenting course(71.0%vs 10.0%,P=0.01).CONCLUSION Utilising a single Kaffes intraductal fully-covered metal stent for at least 4 months is safe and efficacious for the management of post-transplant anastomotic strictures.

ConvNeXt网络及Stacked BiLSTM-Self-Attention在轴承剩余寿命预测中的应用

在滚动轴承剩余使用寿命预测方面,采用传统方法时存在鲁棒性差、精度低等各种问题。近些年来深度学习的发展为解决这些问题提供了新的思路。为了进一步提高对轴承寿命的预测精度,提出了一种基于ConvNeXt网络、堆叠双向长短时记忆网络(SBiLSTM)和自注意力机制(Self-Attention)的滚动轴承寿命预测方法。首先,采用连续小波变换(CWT)构造了振动信号的时频图,以更好地捕捉信号的时域和频域特征;然后,将得到的时频图输入到构建的ConvNeXt网络中,通过卷积、池化和层归一化等操作,对时频图的关键特征进行了提取;最后,将提取后的特征输入到SBiLSTM-Self-Attention模块中,进一步提取了时序信息和特征权重分配数据,利用PHM2012挑战数据集进行了验证,通过实验分析了该方法的均方根误差(RMSE)和平均绝对误差(MAE)。研究结果表明:相较于现有技术方法,该方法的平均RMSE为0.031;与其他三种方法,即卷积神经网络(CNN)、深度残差双向门控循环单元(DRN-BiGRU)和深度卷积自注意力双向门控循环单元(DCNN-Self-Attention-BiGRU)相比,其平均RMSE值分别下降了79%、74%和55%,MAE值分别下降了78%、73%和53%,说明该方法在滚动轴承剩余寿命预测中有较好的性能。

Correlation Analysis between Self-Disclosure and Social Support in Patients with Breast Cancer

Objective: Describe the status quo of self-disclosure and social support in breast cancer patients and analyze the correlation between them. Methods: General data questionnaire, distress disclosure Index scale and Chinese version of medical social support scale were used to investigate the correlation between self-disclosure and social support in breast cancer patients by Pearson correlation analysis. Results: 1) The total self-disclosure score was (38.75 ± 9.18);the total score of social support was (70.57 ± 14.04) scores, including emotional information support dimension (28.39 ± 6.06) scores, practical support dimension (15.62 ± 3.31) scores, elastic support dimension (14.85 ± 3.23) scores, and emotional support dimension (11.70 ± 2.56) scores. 2) Self-disclosure was positively correlated with social support (r = 0.433, p Conclusion: Breast cancer patients had a moderate level of self-disclosure, and the higher the level of self-disclosure, the better the social support. It is suggested that improving the self-disclosure level of breast cancer patients can help them obtain more social support.

Socio-Economic and Health Indicators’ Relation to Self-Assessed Health: A Case Study of Phai Tha Pho, Phichit Province, Thailand

Background: Self-assessed health (SAH) is used as a common method of sociology research to understand the implications of self-reported health and the link to social factors like education, income, and occupation. The paper explores the impact of socio-economic and health indicators on self-assessed health in the middle-aged to the senior population in a rural community in Thailand. Methods: Primary data were collected after conducting a randomized sampling for 100 people using direct interviews in two locations within the sub-district of Phai Tha Pho, Thailand. The target demographic was the middle-age to elderly population. A logit model was applied to the collected samples. Results: The study highlights that higher education, income, and sleep are high predictors for positive SAH while high blood sugar level has significant adverse effects on SAH. Detection of metabolic syndrome further indicates degraded overall health perception over time. Conclusion: The study demonstrated the relationship between socio-economic indicators and illnesses alongside individual SAH in rural Thailand. Accordingly, policies have been proposed that include targeted subsidies for healthy food alternatives, promoting work-rest balance at all levels, and an expansion of sub-district education up to secondary school. SAH can be performed regularly and expanded across communities including areas of low-income living due to its low implementation costs. It could also be used as a tool to support the government’s public health initiatives complementing the existing five-year direct health check-up programme. A comparative study of SAH across regions is recommended for future research.

基于BERT-SELFATT-CNN模型的垃圾邮件分类方法

针对传统垃圾邮件分类方法中使用静态词向量不能解决一词多义、长序列信息特征提取不足等问题,提出了一种基于BERT-SELFATT-CNN模型的垃圾邮件分类方法。使用动态文本表示方法BERT对邮件内容进行预训练,并生成带有上下语义信息的词向量,经过能够并行计算的自注意力机制层计算词与词之间的相似度去挖掘句子长距离信息,将生成的隐藏层向量输入到CNN网络提取向量局部特征。在中文垃圾邮件数据集上与已有模型进行对比实验,结果表明该模型在精确度、召回率和F1值上均有提高,模型训练速度也得到提升。

Effect of Diabetes Self-Management Education on Glycaemic Control in Sudanese Adults with Type 2 Diabetes

Research Background: The high prevalence of diabetes in Sudan, estimated at 16%, highlights the importance of effective health education in diabetes management. Diabetes self-management education has been identified as a crucial tool in enhancing the knowledge, attitudes, and abilities necessary for self-management among individuals with diabetes. Aim: To assess the impact of diabetes self-management education on medication adherence and glycemic control in Sudanese adults with type 2 diabetes before and 3 months after the DSME intervention. Method: The study was conducted in Sudan between September 2022 and March 2023, it was an interventional, one-group, pre- and post-test study that aimed to assess the impact of diabetes self-management education (DSME) on medication adherence and diabetes control in Sudanese adults with type 2 diabetes. The research was conducted in primary health care centers in six cities in Sudan and involved 244 participants. The data entry and statistical analysis were conducted using the Statistical Package for Social Sciences version 27.0. A paired t test was used for analysis. Results: The study included 244 participants, 67% of whom were males. The age mean ± SD was 48.6 ± 9.3 years, and 85.3% of participants were married. Age at onset of diabetes mean ± SD was 40.60 ± 7.81 years;44.6% had diabetes for less than 5 years;and 84.1% had a positive family history of diabetes mellitus. The levels of poor, low, and partial adherence to medication decreased by 8.2%, 4%, and 20.6%, respectively, after the intervention. The levels of good and high medication regime adherence increased by 13% and 19.8%, respectively;BMI decreased by 1.1 ± 0.73 kg/m2 (p = 0.005). The fasting blood sugar decreased by 69 ± 32.9 mg/dl (p = 0.049), and the glycated hemoglobin decreased by 1.21 ± 0.28% (p = 0.001). Conclusions: The findings of this study reinforce the importance of patient education in improving glycemic control and enhancing self-management behaviors. Patient education plays a critical role in enhancing glycemic control and self-management behaviors. It is essential for healthcare providers to adopt a patient-centered approach, taking into account the individual's beliefs, attitudes, and knowledge about their illness and treatment. Overcoming these challenges necessitates a comprehensive approach, including enhancing healthcare professionals’ knowledge and communication skills, offering accessible and culturally sensitive diabetes education programs, and addressing barriers to resources and support for self-management.

基于DCNN网络及Self-Attention-BiGRU机制的轴承剩余寿命预测

深度神经网络在剩余寿命预测(RUL)领域得到了广泛的应用。传统的滚动轴承寿命预测模型存在预测精确度较低、鲁棒性较弱的问题。为了进一步提升预测模型的精确度以及鲁棒性,提出了一种融合深度卷积神经网络(DCNN)、双向门控循环单元(BiGRU)以及自注意力机制(Self-Attention)三种模块的滚动轴承剩余使用寿命预测模型。首先,利用DCNN网络对原始振动信号的时域特征、频域特征进行了提取;然后,使用不确定量化的方法对提取到的特征进行了评价和筛选,利用筛选过后的特征构建了新的替代特征集;最后,利用Self-Attention-BiGRU网络对轴承的剩余使用寿命进行了预测,并在IEEE PHM2012数据集上进行了验证。实验结果表明:相较于BiGRU、GRU和BiLSTM三种模型的预测结果,基于DCNN及Self-Attention-BiGRU方法的预测结果最优,两项误差值:平均绝对误差(MAE)、均方根误差(RMSE)最低,其中工况一的一号轴承RUL预测的MAE值相较于BiGRU、GRU以及BiLSTM网络分别下降了7.0%、7.4%和6.5%,RMSE值相较于其他三种模型分别下降了7.6%、8.4%和6.9%,预测的Score值最高,分值为0.985。通过不同数据集的划分,证明了该方法在轴承RUL预测时的强鲁棒性。实验结果验证了基于DCNN网络及Self-Attention-BiGRU模型在轴承剩余使用寿命预测中的有效性。

Research Progress on Self-Efficacy Level of Patients with Type 2 Diabetes Mellitus and Its Influencing Factors

Self-efficacy plays an important role in the management of type 2 diabetes mellitus (T2DM) patients, and it runs through the whole process of diabetes treatment, which is conducive to controlling and delaying the occurrence and development of complications, as well as improving the quality of life of patients. This paper mainly describes the concept of self-efficacy, the current situation of self-efficacy of diabetic patients at home and abroad, the functional aspects and their influencing factors, so as to take relevant measures on how to improve self-efficacy. It aims to provide a theoretical basis for the development of self-efficacy interventions for patients with type 2 diabetes mellitus.

基于Self-CGRU模型的地铁基坑周边地表沉降预测

为提升地铁基坑开挖引发的地表沉降的预测精度,基于自注意力机制和深度学习提出一种能捕捉沉降数据时空特性的深度注意力组合预测模型(self-attention convolutional gated recurrent units,Self-CGRU)。Self-CGRU模型由空间模块和时间模块搭建。空间模块中,选择卷积神经网络捕捉相邻监测点沉降数据的空间相关性;时间模块中,使用门控循环单元神经网络分析沉降数据的时间规律,并引入自注意力机制捕获沉降数据内部的自相关性,进而得到沉降预测值。选取中国深圳市地铁基坑周边地表沉降数据验证Self-CGRU模型,结果表明:相比现有模型,Self-CGRU模型预测性能更好,使预测精度提高了17.48%~29.17%。研究成果可为地铁基坑周边地表沉降预测提供一种准确且稳定的新模型。

Practice of Glycemic Self-Monitoring in Diabetic Patients Followed at the Endocrinology Department of Donka University Hospital in Guinea

Diabetes is a chronic pathology whose evolution is marked by micro and macroangiopathic complications. Optimal management can prevent the onset of complications and improve patients’ quality of life. Objectives: To determine the frequency of self-monitoring of blood glucose and to describe the errors found during self-monitoring in diabetic patients followed at the Endocrinology Department of Donka University Hospital in Guinea. Materials and methods: Descriptive cross-sectional study carried out between August and September 2020 involving diabetic patients followed up at the Endocrinology and Diabetology Department of the Donka National Hospital, CHU Conakry. Results: A total of 301 patients were enrolled, with an average age of 44.24 ± 21.01 years. 64.12% were female. Type 2 diabetes predominated in 64% of cases. The mean duration of diabetes was 6.14 ± 4.67 years, and 75.08% of patients lived in urban areas. Patients were on insulin in 36.21% of cases, insulin and biguanides (26.25%), hypoglycemic sulfonamide and biguanides (19.27%) and biguanides in 18.27% of cases. The frequency of self-monitoring of blood glucose was 43%, and 38% of patients made errors, notably reusing lancets (60%), not checking the expiration date (55.65%) and not washing their hands (48%). Conclusion: This study shows that self-monitoring of blood glucose is not performed by the majority of patients. Numerous errors were identified during blood glucose testing. Continued therapeutic education on the use of blood glucose meters will help empower patients and improve their quality of life.

基于Self-Attention与Bi-LSTM的大学生情感倾向研究

针对基于词向量的网络模型性能过分依赖分词准确性的问题,提出基于FastText字向量表示方法结合SelfAttention与BiLSTM的大学生情感分析方法(character-SATT-BiLSTM)。使用fasttext模型生成文本字向量,通过双向长短时记忆模型提取上下文语义特征,利用自注意力机制强化关键信息,最后使用Softmax分类器判断情感类别。实验结果显示,字向量文本表示方法比词向量更适合论坛文本情感分类,同时character-SATT-BiLSTM相比characterLSTM、character-BiLSTM等模型的效果更优,分类性能分别提高了6%和3%。

战略企业家推进高水平科技自立自强的角色价值与作用机制

战略企业家是国家战略人才力量的重要组成部分,在推进高水平科技自立自强过程中具有无可替代的价值。战略企业家既是企业家发展边界的突破,也是企业家队伍建设的未来指向。基于企业家理论及我国优秀企业家、优秀企业的实践基础,从角色期待视角对中国情境下战略企业家的内涵进行创新性界定,刻画战略企业家的典型特征,探讨战略企业家与一般企业家的异同,整体上形成对战略企业家的新认知。在此基础上,剖析战略企业家在实现高水平科技自立自强过程中的战略人才价值和创新人才价值,发现战略企业家通过涓滴效应、同群效应、桥梁效应在企业、产业链、创新联合体等场域发挥重要作用。系统回答了战略企业家“是谁—有何价值—如何发挥价值”的问题,研究成果可为新时代战略企业家助推高水平科技自立自强提供理论支撑和实践指导。