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.

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.

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.

Design of multifunctional polymeric binders in silicon anodes for lithium‐ion batteries

Silicon(Si)is a promising anode material for lithium‐ion batteries(LIBs)owing to its tremendously high theoretical storage capacity(4200 mAh g−1),which has the potential to elevate the energy of LIBs.However,Si anodes exhibit severe volume change during lithiation/delithiation processes,resulting in anode pulverization and delamination with detrimental growth of solid electrolyte interface layers.As a result,the cycling stability of Si anodes is insufficient for commercialization in LIBs.Polymeric binders can play critical roles in Si anodes by affecting their cycling stability,although they occupy a small portion of the electrodes.This review introduces crucial factors influencing polymeric binders'properties and the electrochemical performance of Si anodes.In particular,we emphasize the structure–property relationships of binders in the context of molecular design strategy,functional groups,types of interactions,and functionalities of binders.Furthermore,binders with additional functionalities,such as electrical conductivity and self‐healability,are extensively discussed,with an emphasis on the binder design principle.

Frequency Domain Fatigue Evaluation on SCR Girth-Weld Based on Structural Stress

The Steel Catenary Riser(SCR)is a vital component for transporting oil and gas from the seabed to the floating platform.The harsh environmental conditions and complex platform motion make the SCR’s girth-weld prone to fatigue failure.The structural stress fatigue theory and Master S-N curve method provide accurate predictions for the fatigue damage on the welded joints,which demonstrate significant potential and compatibility in multi-axial and random fatigue evaluation.Here,we propose a new frequency fatigue model subjected to welded joints of SCR under multiaxial stress,which fully integrates the mesh-insensitive structural stress and frequency domain random process and transforms the conventional welding fatigue technique of SCR into a spectrum analysis technique utilizing structural stress.Besides,a full-scale FE model of SCR with welds is established to obtain the modal structural stress of the girth weld and the frequency response function(FRF)of modal coordinate,and a biaxial fatigue evaluation about the girth weld of the SCR can be achieved by taking the effects of multi-load correlation and pipe-soil interaction into account.The research results indicate that the frequency-domain fatigue results are aligned with the time-domain results,meeting the fatigue evaluation requirements of the SCR.

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.

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.

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.

Health education intervention and its impact on patients with unilateral vestibular dysfunction:An information-motivation-behavioral model approach

Unilateral vestibular dysfunction is a one-sided impairment of vestibular function in one ear.Incorporating health education in treatment and rehabilitation plans can improve vestibular function,keep negative emotions at bay,and reduce the extent of the condition.This letter investigates the impact of the informationmotivation-behavioral skills model as a medium for health education on patient outcomes.While offering encouraging observations,there are certain limitations,such as the study’s retrospective design,small sample size,use of subjective measures,and lack of longer follow-ups that challenge the cogency of the study.The study is a step toward transforming vestibular dysfunction treatment through health education.

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.

Haemocompatibility of Ti-3Zr-2Sn-3Mo-25Nb biomedical alloy with surface heparinization using electrostatic self assembly technology

The haemocompatibility of Ti-3Zr-2Sn-3Mo-25Nb biomedical alloy was studied after surface heparinization. A layer of sol-gel TiO2 films was applied on the alloy samples followed by active treatment in the bio-functionalized solution for introducing the OH- and groups, and then the heparin was immobilized on the active TiO2 films through the electrostatic self assembly technology. It is shown that the heparinized films are mainly composed of anatase and rutile with smooth and dense surface. In vitro blood compatibility was evaluated by haemolysis test, clotting time and platelet adhesion behavior tests. The results show that the haemocompatibility of the alloy could be significantly improved by surface heparinization.

A Comparative Study on the Quality Components Between Self Pollinated Seeds and Naturally Pollinated Seeds in Brassica napus L. in Sichuan Ecological Region

[ Objective] This study was to reveal the differences in crude fat and glucosinolates between self pollinated seeds and naturally pollinated seeds in Brassica napus in sichuan ecological region.. [ Method] Near-infrared spectroscopy method （NIRS） was employed to measure the quality components in self pollinated seeds and naturally pollinated seeds of 861 shares of Brassica napus from Sichuan ecological region. And correlation analysis and regression analysis were conducted based on the experimental data via SPSS （statistics package for social science）. [ Result] The contents of crude fat in the self pollinated seeds were commonly a higher than that in the naturally pollinated seeds at 0.01 significant level; while the contents of glucosinolates in the self pollinated seeds and the naturally pollinated seeds were insignificantly different. Both the correlation relationship and linear regression for the crude fat between the self pollinated seeds and naturally pollinated seeds reached the significant level. The regression equations for the contents of crude fat（y1 ） and glucosinolates（ y2 ） in the naturally pollinated seeds and of crude fat（ x1 ） and glucosinolates（ x2 ） in self pollinated seeds were respectively determined to be y1 = 16.844 ＋0.614x1 and y2 = -0.620 ＋ 1.017 x2. [ Conclusion] In Brassica napus breeding, crude fat in naturally pollinated seeds should be emphatically taken into account, meanwhile concurrently considering that in self pollinated seeds; while glucosinolates in both the self pollinated seeds and the naturally pollinated seeds must be simultaneously concerned.

Self tempering effect of near eutectic Al-Si casting with different wall thickness solidified and cooled in permanent die

In order to study the self tempering effect on the solidification of Al-Si alloy, a setup was designed to conduct experiments. The characters of β phases in different thicknesses of Al-Si samples were investigated. The results show that the size distributions of β phases obey the logarithmic normal distribution. The Brinell hardness tests were also carried out. The tested hardness results show that the hardness distribution of the casting cooled in water is evener than that cooled in air, and its averaged value is higher than that cooled in air.

A Recursive Algorithm for Offspring's Genotype Frequency of Selfing Population on Multiple Alleles with Limited Loci

This paper studies the offspring＇s genotype frequency of the selfing population on multiple alleles with limited loci.A recursive algorithm is given for it.It is discovered that the genotype frequency of homozygous gene of limited loci increases by generations.Relative increment reduces by generations and the genotype frequency tends to a definite value finally.The genotype frequency of limited loci with hybrid gene tends to 0 finally.But it is possibility that the genotype frequency increases in previous generations then reduces later.It is found that the number of the hybrid gene are more,the speeds tending to 0 are quicker.

Self Adjusting Feedforward Compensation Tracking Control for Proportional Valve Controlled Motor

Aim Aiming at the position tracking control for valve controlled motor electrohydraulic proportional servo systems mainly driving the static load torque, the tracking performance was studied in the presence of the variable gain and deadzone. Methods On the basis of conventional composite control with the deadzone compensation method, a comprehensive control approach with the deadzone and self adjusting feedforward compensation was proposed. Results Experimental results showed that the good tracking performance was achieved for the sinusoidal and constant velocity position tracking under a wide variations of load torque. Conclusion The position tracking accuracy for valve controlled motor electrohydraulic proportional servo systems has been solved by using the comprehensive control approach with the deadzone and self adjusting feedforward compensation.

New Structural Self-Organizing Fuzzy CMAC with Basis Functions

To improve the nonlinear approximating ability of cerebellar model articulation controller(CMAC), by introducing the Gauss basis functions and the similarity measure based addressing scheme, a new kind of fuzzy CMAC with Gauss basis functions(GFCMAC) was presented. Moreover, based upon the improvement of the self organizing feature map algorithm of Kohonen, the structural self organizing algorithm for GFCMAC(SOGFCMAC) was proposed. Simulation results show that adopting the Gauss basis functions and fuzzy techniques can remarkably improve the nonlinear approximating capacity of CMAC. Compared with the traditional CMAC,CMAC with general basis functions and fuzzy CMAC(FCMAC), SOGFCMAC has the obvious advantages in the aspects of the convergent speed, approximating accuracy and structural self organizing.