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.

Handily etching nickel foams into catalyst-substrate fusion self‐stabilized electrodes toward industrial‐level water electrolysis

The key challenge of industrial water electrolysis is to design catalytic electrodes that can stabilize high current density with low power consumption(i.e.,overpotential),while industrial harsh conditions make the balance between electrode activity and stability more difficult.Here,we develop an efficient and durable electrode for water oxidation reaction(WOR),which yields a high current density of 1000 mA cm−2 at an overpotential of only 284 mV in 1M KOH at 25°C and shows robust stability even in 6M KOH strong alkali with an elevated temperature up to 80°C.This electrode is fabricated from a cheap nickel foam(NF)substrate through a simple one-step solution etching method,resulting in the growth of ultrafine phosphorus doped nickel-iron(oxy)hydroxide[P-(Ni,Fe)O_(x)H_(y)]nanoparticles embedded into abundant micropores on the surface,featured as a self-stabilized catalyst–substrate fusion electrode.Such self-stabilizing effect fastens highly active P-(Ni,Fe)O_(x)H_(y)species on conductive NF substrates with significant contribution to catalyst fixation and charge transfer,realizing a win–win tactics for WOR activity and durability at high current densities in harsh environments.This work affords a cost-effective WOR electrode that can well work at large current densities,suggestive of the rational design of catalyst electrodes toward industrial-scale water electrolysis.

Short‐term and long‐term memory self‐attention network for segmentation of tumours in 3D medical images

Tumour segmentation in medical images(especially 3D tumour segmentation)is highly challenging due to the possible similarity between tumours and adjacent tissues,occurrence of multiple tumours and variable tumour shapes and sizes.The popular deep learning‐based segmentation algorithms generally rely on the convolutional neural network(CNN)and Transformer.The former cannot extract the global image features effectively while the latter lacks the inductive bias and involves the complicated computation for 3D volume data.The existing hybrid CNN‐Transformer network can only provide the limited performance improvement or even poorer segmentation performance than the pure CNN.To address these issues,a short‐term and long‐term memory self‐attention network is proposed.Firstly,a distinctive self‐attention block uses the Transformer to explore the correlation among the region features at different levels extracted by the CNN.Then,the memory structure filters and combines the above information to exclude the similar regions and detect the multiple tumours.Finally,the multi‐layer reconstruction blocks will predict the tumour boundaries.Experimental results demonstrate that our method outperforms other methods in terms of subjective visual and quantitative evaluation.Compared with the most competitive method,the proposed method provides Dice(82.4%vs.76.6%)and Hausdorff distance 95%(HD95)(10.66 vs.11.54 mm)on the KiTS19 as well as Dice(80.2%vs.78.4%)and HD95(9.632 vs.12.17 mm)on the LiTS.

A Graph Theory Based Self-Learning Honeypot to Detect Persistent Threats

Attacks on the cyber space is getting exponential in recent times.Illegal penetrations and breaches are real threats to the individuals and organizations.Conventional security systems are good enough to detect the known threats but when it comes to Advanced Persistent Threats(APTs)they fails.These APTs are targeted,more sophisticated and very persistent and incorporates lot of evasive techniques to bypass the existing defenses.Hence,there is a need for an effective defense system that can achieve a complete reliance of security.To address the above-mentioned issues,this paper proposes a novel honeypot system that tracks the anonymous behavior of the APT threats.The key idea of honeypot leverages the concepts of graph theory to detect such targeted attacks.The proposed honey-pot is self-realizing,strategic assisted which withholds the APTs actionable tech-niques and observes the behavior for analysis and modelling.The proposed graph theory based self learning honeypot using the resultsγ(C(n,1)),γc(C(n,1)),γsc(C(n,1))outperforms traditional techniques by detecting APTs behavioral with detection rate of 96%.

CoLM^(2)S:Contrastive self‐supervised learning on attributed multiplex graph network with multi‐scale information

Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of the real‐word system are multiple relations,where entities are linked by different types of relations,and each relation is a view of the graph network.Second,the rich multi‐scale information(structure‐level and feature‐level)of the graph network can be seen as self‐supervised signals,which are not fully exploited.A novel contrastive self‐supervised representation learning framework on attributed multiplex graph networks with multi‐scale(named CoLM^(2)S)information is presented in this study.It mainly contains two components:intra‐relation contrast learning and interrelation contrastive learning.Specifically,the contrastive self‐supervised representation learning framework on attributed single‐layer graph networks with multi‐scale information(CoLMS)framework with the graph convolutional network as encoder to capture the intra‐relation information with multi‐scale structure‐level and feature‐level selfsupervised signals is introduced first.The structure‐level information includes the edge structure and sub‐graph structure,and the feature‐level information represents the output of different graph convolutional layer.Second,according to the consensus assumption among inter‐relations,the CoLM^(2)S framework is proposed to jointly learn various graph relations in attributed multiplex graph network to achieve global consensus node embedding.The proposed method can fully distil the graph information.Extensive experiments on unsupervised node clustering and graph visualisation tasks demonstrate the effectiveness of our methods,and it outperforms existing competitive baselines.

Diabetes self-management education:Benefits and challenges

Diabetes mellitus has spread throughout many nations of the world and is now a serious threat.A lack of patient self‑management has been linked to this drain on global health.The consequences of diabetic patients’poor self‑management have increased a variety of complications and lengthened hospital stays.Poor information and skill acquisition have been linked to poor self‑management.Participating in a co‑operative approach known as diabetes self‑management education will help diabetes patients who want to successfully self‑manage their condition and any associated conditions.Information is one of the most important components of a diabetes management strategy.In conclusion,numerous studies have shown that patients with diabetes have poor self‑management skills and knowledge in all areas,making training in diabetes self‑management necessary to minimize the complications that may result from diabetes mellitus among the patients.This review discussed the severity of diabetes mellitus,diabetes self‑management,and the benefits and challenges of diabetes self‑management,which may aid individuals in understanding the significance of diabetes self‑management and how it relates to diabetes self‑care.

Ext-ICAS:A Novel Self-Normalized Extractive Intra Cosine Attention Similarity Summarization

With the continuous growth of online news articles,there arises the necessity for an efficient abstractive summarization technique for the problem of information overloading.Abstractive summarization is highly complex and requires a deeper understanding and proper reasoning to come up with its own summary outline.Abstractive summarization task is framed as seq2seq modeling.Existing seq2seq methods perform better on short sequences;however,for long sequences,the performance degrades due to high computation and hence a two-phase self-normalized deep neural document summarization model consisting of improvised extractive cosine normalization and seq2seq abstractive phases has been proposed in this paper.The novelty is to parallelize the sequence computation training by incorporating feed-forward,the self-normalized neural network in the Extractive phase using Intra Cosine Attention Similarity(Ext-ICAS)with sentence dependency position.Also,it does not require any normalization technique explicitly.Our proposed abstractive Bidirectional Long Short Term Memory(Bi-LSTM)encoder sequence model performs better than the Bidirectional Gated Recurrent Unit(Bi-GRU)encoder with minimum training loss and with fast convergence.The proposed model was evaluated on the Cable News Network(CNN)/Daily Mail dataset and an average rouge score of 0.435 was achieved also computational training in the extractive phase was reduced by 59%with an average number of similarity computations.

一种基于用户行为的Self集构造和演化方法

对计算机安全免疫系统中Self集构造的可能性和特征码选取的原则进行了探讨性的分析 ,提出了一种基于用户行为的Self集构造方法。Self集中的特征码包含了用户的操作、操作对象和操作参数。

Predictors of permanent pacemaker implantation following transcatheter aortic valve replacement-the search is still on!

Several anatomical,demographic,clinical,electrocardiographic,procedural,and valve-related variables can be used to predict the probability of developing con-duction abnormalities after transcatheter aortic valve replacement(TAVR)that necessitate permanent pacemaker(PPM)implantation.These variables include calcifications around the device landing zone and in the mitral annulus;pre-existing electrocardiographic abnormalities such as left and right bundle branch blocks(BBB),first-and second-degree atrioventricular blocks,as well as bifas-cicular and trifascicular blocks;male sex;diabetes mellitus(DM);hypertension;history of atrial fibrillation;renal failure;dementia;and use of self-expanding valves.The current study supports existing literature by demonstrating that type 2 DM and baseline right BBB are significant predictors of PPM implantation post-TAVR.Regardless of the side of the BBB,this study demonstrated,for the first time,a linear association between the incidence of PPM implantation post-TAVR and every 20 ms increase in baseline QRS duration(above 100 ms).After a 1-year follow-up,patients who received PPM post-TAVR had a higher rate of hospital-ization for heart failure and nonfatal myocardial infarction.

Evaluating the influence of a structured nursing protocol on targeted outcomes in rheumatoid arthritis patients

Objective:Rheumatoid arthritis(RA)requires comprehensive management.Structured nursing protocols may enhance outcomes,but evidence is limited.This study evaluated the effect of a structured nursing protocol on RA outcomes.Materials and Methods:In this one-group pre-post study,30 Egyptian RA patients completed assessments before and after a 12-week nursing protocol comprising education,psychosocial support,and self-management promotion.Assessments included clinical evaluation of joint counts,erythrocyte sedimentation rate(ESR),and C-reactive protein(CRP)and patient-reported Arthritis Self-Efficacy Scale(ASES),Health Assessment Questionnaire(HAQ),Visual Analog Scale(VAS)for pain,and Hospital Anxiety and Depression Scale(HADS).Results:The study demonstrated significant improvements in both clinical-and patient-reported outcomes.Joint count decreased from 18.4±4.2 to 14.2±3.8(P<0.001),ESR from 30.1±6.8 mm/h to 25.5±6.8 mm/h(P<0.01),and CRP levels from 15.2±3.6 mg/L to 11.8±2.9 mg/L(P<0.01)postintervention.Patient-reported outcomes showed a marked increase in ASES score from 140±25 to 170±30(P<0.001)and reductions in HAQ from 1.6±0.4 to 1.3±0.3(P<0.01),VAS pain score from 7.8±1.7 to 6.2±1.2(P<0.001),and HADS anxiety and depression scores from 11±3 to 8±2(P<0.05)and 10±2 to 7±1(P<0.05),respectively.Conclusion:A structured nursing protocol significantly improved clinical disease activity,physical functioning,pain,self-efficacy,and emotional well-being in RA patients.A multifaceted nursing intervention appears beneficial for optimizing RA outcomes.

基于Rough集理论的Self集的构造和演化算法

提出了一种基于Rough集理论的Self集构造和演化算法。利用Rough集约简算法,对用户的安全访问行为的数据作规范化处理并进行约简,从中提取有效的最简规则,降低了安全数据的冗余,减轻了特征码构造的负担。使用Rough集上、下近似集原理,构造了上、下近似Self集,实现了Self的优化和扩展,有效地解决了Self集的自动演化问题。

新的多位self-shrinking序列的构造

给出了一种多位自收缩序列,并解决了多位自收缩序列的周期下界,线性复杂度,最后构造了新的多位self-shrinking序列模型.

On Our Peculiar Black Hole Universe

The black hole model of the Universe evolution, accompanied by matter creation, already successfully accounting for many features of the past is discussed and further justified. It is once more stressed that even a very large object but with a big mass is in its own right a black hole. As a consequence, the extrapolation of the past predicts for the future no big crunch, nor big bounce but a steady expansion with smaller matter density.

Mrs.Dalloway:A Journey of Self-Discovery

Mrs.Dalloway has two stories about the same woman.Mrs.Dalloway is her social self,busy with her party,seemingly happy but with some hidden problems.The individual self as Clarissa is lost in deep thought of her true self.The textual analysis will apply Lacan's theory of name-of-the-father or symbolic order to explore the causes of Clarissa's problematic social self.It concludes that the protagonist begins the process of self-discovery by thinking about and talking with her close friends,trying to dig out her individual self which is suppressed by social self.

《悦己SELF》的“快乐文化”传播之道

《悦己SELF》在当前中国期刊市场的登陆,带给我们的是新时代女性的"快乐由自己"的精神实质,其特有的"悦己"语录诠释着刊物营销的创新方略,版权合作下的"中国化"探求初显媒介传播特质,纸质媒介和网络社区门户的互动传播开启了新媒体时代杂志营销的新路径。

Self集的动态更新方法

分析了人工免疫系统中识别器的产生成熟以及对抗原的识别过程,认为通过不断对self集合定义进行修正可以提高系统识别能力和降低伪肯定率。提出了一套动态更新self集合的机制,使得系统能够在不中断运行的情况下对self的更新做出响应。

OXIDATION RESISTANCE AND SELF-SEALING PROPERTY OF CERAMIC COATINGS FOR C/C COMPOSITES

Ｃａｒｂｏｎ／Ｃａｒｂｏｎ（Ｃ／Ｃ）ｃｏｍｐｏｓｉｔｅｍａｔｅｒｉａｌｓｏｆｅｒｍａｎｙａｄｖａｎｔａｇｅｓｏｖｅｒｔｒａｄｉｔｉｏｎａｌｍａｔｅｒｉ－ａｌｓ．Ｐｒｉｍａｒｉｌｙ，ｔｈｅｌａｒｇｅｓｔｒｅｎｇｔｈ－ｔｏ－ｗｅｉｇｈｔｒａ－ｔｉｏ，ａｎ...

Self Organization Map for Clustering and Classification in the Ecology of Agent Organizations

Development of computational agent organizations or “societies” has become the domiant computing paradigm in the arena of Distributed Artificial Intelligence, and many foreseeable future applications need agent organizations, in which diversified agents cooperate in a distributed manner, forming teams. In such scenarios, the agents would need to know each other in order to facilitate the interactions. Moreover, agents in such an environment are not statically defined in advance but they can adaptively enter and leave an organization. This begs the question of how agents locate each other in order to cooperate in achieving organizational goals. Locating agents is a quite challenging task, especially in organizations that involve a large number of agents and where the resource avaiability is intermittent. The authors explore here an approach based on self organization map (SOM) which will serve as a clustering method in the light of the knowledge gathered about various agents. The approach begins by categorizing agents using a selected set of agent properties. These categories are used to derive various ranks and a distance matrix. The SOM algorithm uses this matrix as input to obtain clusters of agents. These clusters reduce the search space, resulting in a relatively short agent search time.