SDGNN: Symmetry-Preserving Dual-Stream Graph Neural Networks

Dear Editor,This letter proposes a symmetry-preserving dual-stream graph neural network(SDGNN) for precise representation learning to an undirected weighted graph(UWG). Although existing graph neural networks(GNNs) are influential instruments for representation learning to a UWG, they invariably adopt a unique node feature matrix for illustrating the sole node set of a UWG.

Privacy-Preserving Consensus-Based Distributed Economic Dispatch of Smart Grids via State Decomposition

This paper studies the privacy-preserving distributed economic dispatch(DED)problem of smart grids.An autonomous consensus-based algorithm is developed via local data exchange with neighboring nodes,which covers both the islanded mode and the grid-connected mode of smart grids.To prevent power-sensitive information from being disclosed,a privacy-preserving mechanism is integrated into the proposed DED algorithm by randomly decomposing the state into two parts,where only partial data is transmitted.Our objective is to develop a privacy-preserving DED algorithm to achieve optimal power dispatch with the lowest generation cost under physical constraints while preventing sensitive information from being eavesdropped.To this end,a comprehensive analysis framework is established to ensure that the proposed algorithm can converge to the optimal solution of the concerned optimization problem by means of the consensus theory and the eigenvalue perturbation approach.In particular,the proposed autonomous algorithm can achieve a smooth transition between the islanded mode and the grid-connected mode.Furthermore,rigorous analysis is given to show privacy-preserving performance against internal and external eavesdroppers.Finally,case studies illustrate the feasibility and validity of the developed algorithm.

2P3FL:A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages.As a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model.However,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy.To address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global convergence.Resultantly improves the privacy of the local models.This analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)datasets.Precision,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and FedProx.The experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.

A Cloud-Fog Enabled and Privacy-Preserving IoT Data Market Platform Based on Blockchain

The dynamic landscape of the Internet of Things(IoT)is set to revolutionize the pace of interaction among entities,ushering in a proliferation of applications characterized by heightened quality and diversity.Among the pivotal applications within the realm of IoT,as a significant example,the Smart Grid(SG)evolves into intricate networks of energy deployment marked by data integration.This evolution concurrently entails data interchange with other IoT entities.However,there are also several challenges including data-sharing overheads and the intricate establishment of trusted centers in the IoT ecosystem.In this paper,we introduce a hierarchical secure data-sharing platform empowered by cloud-fog integration.Furthermore,we propose a novel non-interactive zero-knowledge proof-based group authentication and key agreement protocol that supports one-to-many sharing sets of IoT data,especially SG data.The security formal verification tool shows that the proposed scheme can achieve mutual authentication and secure data sharing while protecting the privacy of data providers.Compared with previous IoT data sharing schemes,the proposed scheme has advantages in both computational and transmission efficiency,and has more superiority with the increasing volume of shared data or increasing number of participants.

Privacy-Preserving Healthcare and Medical Data Collaboration Service System Based on Blockchain and Federated Learning

As the volume of healthcare and medical data increases from diverse sources,real-world scenarios involving data sharing and collaboration have certain challenges,including the risk of privacy leakage,difficulty in data fusion,low reliability of data storage,low effectiveness of data sharing,etc.To guarantee the service quality of data collaboration,this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning,termed FL-HMChain.This system is composed of three layers:Data extraction and storage,data management,and data application.Focusing on healthcare and medical data,a healthcare and medical blockchain is constructed to realize data storage,transfer,processing,and access with security,real-time,reliability,and integrity.An improved master node selection consensus mechanism is presented to detect and prevent dishonest behavior,ensuring the overall reliability and trustworthiness of the collaborative model training process.Furthermore,healthcare and medical data collaboration services in real-world scenarios have been discussed and developed.To further validate the performance of FL-HMChain,a Convolutional Neural Network-based Federated Learning(FL-CNN-HMChain)model is investigated for medical image identification.This model achieves better performance compared to the baseline Convolutional Neural Network(CNN),having an average improvement of 4.7%on Area Under Curve(AUC)and 7%on Accuracy(ACC),respectively.Furthermore,the probability of privacy leakage can be effectively reduced by the blockchain-based parameter transfer mechanism in federated learning between local and global models.

Insights into upper blepharoplasty: Conservative volume-preserving techniques

This editorial commentary critically examines the systematic review by Miotti et al,which discusses the evolving trends in upper lid blepharoplasty towards a conservative,volume-preserving approach.The review emphasizes the shift from traditional tissue resection to techniques that maintain anatomical integrity,paralleling broader trends in panfacial rejuvenation.Miotti et al delve into the nuances of fat pad management,advocating for conservation over reduction to sustain natural contours and improve long-term aesthetic outcomes.This perspective is supported by comparative studies and empirical data,such as those from Massry and Alghoul et al,highlighting the benefits of conservative approaches in terms of patient satisfaction and aesthetic longevity.The review also stresses the importance of surgeon discretion in adapting procedures to diverse patient demographics,particularly in addressing distinct features such as the Asian upper eyelid.However,it identifies a significant gap in long-term comparative research,underscoring the need for future studies to substantiate the safety and efficacy of these minimalist techniques.Overall,Miotti et al.'s work contributes profoundly to the discourse on personalized,conservative cosmetic surgery,urging ongoing research to refine and validate surgical best practices in upper eyelid blepharoplasty.

Bipolar hip arthroplasty using conjoined tendon preserving posterior lateral approach in treatment of displaced femoral neck fractures

BACKGROUND Hip fractures account for 23.8%of all fractures in patients over the age of 75 years.More than half of these patients are older than 80 years.Bipolar hemiarthroplasty(BHA)was established as an effective management option for these patients.Various approaches can be used for the BHA procedure.However,there is a high risk of postoperative dislocation.The conjoined tendon-preserving posterior(CPP)lateral approach was introduced to reduce postoperative dislocation rates.AIM To evaluate the effectiveness and safety of the CPP lateral approach for BHA in elderly patients.METHODS We retrospectively analyzed medical data from 80 patients with displaced femoral neck fractures who underwent BHA.The patients were followed up for at least 1 year.Among the 80 patients,57(71.3%)were female.The time to operation averaged 2.3 d(range:1-5 d).The mean age was 80.5 years(range:67-90 years),and the mean body mass index was 24.9 kg/m^(2)(range:17-36 kg/m^(2)).According to the Garden classification,42.5%of patients were typeⅢand 57.5%of patients were typeⅣ.Uncemented bipolar hip prostheses were used for all patients.Torn conjoined tendons,dislocations,and adverse complications during and after surgery were recorded.RESULTS The mean postoperative follow-up time was 15.3 months(range:12-18 months).The average surgery time was 52 min(range:40-70 min)with an average blood loss of 120 mL(range:80-320 mL).The transfusion rate was 10%(8 of 80 patients).The gemellus inferior was torn in 4 patients(5%),while it was difficult to identify in 2 patients(2.5%)during surgery.The posterior capsule was punctured by the fractured femoral neck in 3 patients,but the conjoined tendon and the piriformis tendon remained intact.No patients had stem varus greater than 3 degrees or femoral fracture.There were no patients with stem subsidence more than 5 mm at the last follow-up.No postoperative dislocations were observed throughout the follow-up period.No significance was found between preoperative and postoperative mean Health Service System scores(87.30±2.98 vs 86.10±6.10,t=1.89,P=0.063).CONCLUSION The CPP lateral approach can effectively reduce the incidence of postoperative dislocation without increasing perioperative complications.For surgeons familiar with the posterior lateral approach,there is no need for additional surgical instruments,and it does not increase surgical difficulty.

An Arbitrarily High Order and Asymptotic Preserving Kinetic Scheme in Compressible Fluid Dynamic

We present a class of arbitrarily high order fully explicit kinetic numerical methods in compressible fluid dynamics,both in time and space,which include the relaxation schemes by Jin and Xin.These methods can use the CFL number larger or equal to unity on regular Cartesian meshes for the multi-dimensional case.These kinetic models depend on a small parameter that can be seen as a"Knudsen"number.The method is asymptotic preserving in this Knudsen number.Also,the computational costs of the method are of the same order of a fully explicit scheme.This work is the extension of Abgrall et al.(2022)[3]to multidimensional systems.We have assessed our method on several problems for two-dimensional scalar problems and Euler equations and the scheme has proven to be robust and to achieve the theoretically predicted high order of accuracy on smooth solutions.

Clinical Observation on the Treatment of Diabetic Kidney Disease with Damp-heat Stasis Syndrome in Clinical Proteinuria Stage by Kunkui Kidney Preserving Paste

[Objectives]To evaluate the clinical efficacy and safety of Kunkui Kidney Preserving Paste in the treatment of diabetic kidney disease(DKD)patients with damp-heat stasis syndrome in the clinical proteinuria stage.[Methods]Retrospective analysis was made on 30 patients with DKD who were diagnosed with damp-heat stasis syndrome in the clinical proteinuria stage from March 2021 to March 2023 in Jiangsu Province Hospital of Chinese Medicine,and who took Kunkui Kidney Preserving Paste continuously for six months.The urinary albumin/creatinine ratio(UACR),urinary complement C3,and urea nitrogen(BUN)of DKD patients before and after treatment were compared,and estimated glomerular filtration rate(eGFR),blood creatinine(Scr),and cystatin C(CysC)were estimated,and the therapeutic effects on renal function and urinary protein were evaluated.[Results]After treatment,UACR significantly decreased(P<0.01),and urinary complement C3 and Scr decreased(P<0.05),while other indicators showed no significant statistical difference(P>0.05).In terms of evaluating the efficacy of urinary protein therapy,8 cases showed recent relief;8 cases showed significant effect;9 cases were effective,and 5 cases were invalid after treatment,with a total effective rate of 83.33%.In terms of renal function efficacy evaluation,8 cases showed significant effect;4 cases were effective;11 cases were stable,and 7 cases were invalid,with a total effective rate of 76.67%.In the safety evaluation,there were no obvious adverse reactions.[Conclusions]The Kunkui Kidney Preserving Past has significant clinical efficacy and safety in treating DKD patients with damp-heat stasis syndrome in the clinical proteinuria period.It has significant advantages in reducing urinary protein and protecting renal function,which is worthy of clinical promotion.

Ship recognition based on HRRP via multi-scale sparse preserving method

In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.

Analysis of quality of life in patients after transgastric natural orifice transluminal endoscopic gallbladder-preserving surgery

BACKGROUND At present,laparoscopic cholecystectomy(LC)is the main surgical treatment for gallstones.But,after gallbladder removal,there are many complications.Therefore,it is hoped to remove stones while preserving the function of the gallbladder,and with the development of endoscopic technology,natural orifice transluminal endoscopic surgery came into being.AIM To compare the quality of life,perioperative indicators,adverse events after LC and transgastric natural orifice transluminal endoscopic gallbladder-preserving surgery(EGPS)in patients with gallstones.METHODS Patients who were admitted to The First Affiliated Hospital of Xinjiang Medical University from 2020 to 2022 were retrospectively collected.We adopted propen-sity score matching(1:1)to compare EGPS and LC patients.RESULTS A total of 662 cases were collected,of which 589 cases underwent LC,and 73 cases underwent EGPS.Propensity score matching was performed,and 40 patients were included in each of the groups.In the EGPS group,except the gastr-ointestinal defecation(P=0.603),the total score,physical well-being,mental well-being,and gastrointestinal digestion were statistically significant compared with the preoperative score after surgery(P<0.05).In the LC group,except the mental well-being,the total score,physical well-being,gastrointestinal digestion,the gastrointestinal defecation was statistically significant compared with the preoperative score after surgery(P<0.05).When comparing between groups,gastrointestinal defecation had significantly difference(P=0.002)between the two groups,there was no statistically significant difference in the total postoperative score and the other three subscales.In the surgery duration,hospital stay and cost,LC group were lower than EGPS group.The recurrence factors of gallstones after EGPS were analyzed:and recurrence was not correlated with gender,age,body mass index,number of stones,and preoperative score.CONCLUSION Whether EGPS or LC,it can improve the patient’s symptoms,and the EGPS has less impact on the patient’s defecation.It needed to,prospective,multicenter,long-term follow-up,large-sample related studies to prove.

Temporally Preserving Latent Variable Models:Offline and Online Training for Reconstruction and Interpretation of Fault Data for Gearbox Condition Monitoring

Latent variable models can effectively determine the condition of essential rotating machinery without needing labeled data.These models analyze vibration data via an unsupervised learning strategy.Temporal preservation is necessary to obtain an informative latent manifold for the fault diagnosis task.In a temporalpreserving context,two approaches exist to develop a condition-monitoring methodology:offline and online.For latent variable models,the available training modes are not different.While many traditional methods use offline training,online training can dynamically adjust the latent manifold,possibly leading to better fault signature extraction from the vibration data.This study explores online training using temporal-preserving latent variable models.Within online training,there are two main methods:one focuses on reconstructing data and the other on interpreting the data components.Both are considered to evaluate how they diagnose faults over time.Using two experimental datasets,the study confirms that models from both training modes can detect changes in machinery health and identify faults even under varying conditions.Importantly,the complementarity of offline and online models is emphasized,reassuring their versatility in fault diagnostics.Understanding the implications of the training approach and the available model formulations is crucial for further research in latent variable modelbased fault diagnostics.

Monotonicity-Preserving激波捕捉格式在湍流大尺度模拟中的评估

对高阶激波捕捉格式的性能进行了系统的测评,重点分析了Suresh和Huynh(1997)所提出的Monotonicity-Preserving格式的性能.结果表明Monotonicity-Preserving格式的性能显著优于原始WENO(Weighted Essentially Non-Oscillatory)格式,和改进型WENO格式相当.对格式的分析进一步表明,迎风型的激波捕捉格式在湍流模拟方面的性能都不及高阶中心格式,其原因归结为激波捕捉格式所包含的线性和非线性耗散.因此,改进高阶激波捕捉格式的关键在于同时降低格式的线性耗散和非线性耗散,以提高格式对湍流脉动能量的保持和对小尺度脉动结构的捕捉能力.

SELF-DEPENDENT LOCALITY PRESERVING PROJECTION WITH TRANSFORMED SPACE-ORIENTED NEIGHBORHOOD GRAPH

Locality preserving projection （LPP） is a typical and popular dimensionality reduction （DR） method,and it can potentially find discriminative projection directions by preserving the local geometric structure in data. However,LPP is based on the neighborhood graph artificially constructed from the original data,and the performance of LPP relies on how well the nearest neighbor criterion work in the original space. To address this issue,a novel DR algorithm,called the self-dependent LPP （sdLPP） is proposed. And it is based on the fact that the nearest neighbor criterion usually achieves better performance in LPP transformed space than that in the original space. Firstly,LPP is performed based on the typical neighborhood graph; then,a new neighborhood graph is constructed in LPP transformed space and repeats LPP. Furthermore,a new criterion,called the improved Laplacian score,is developed as an empirical reference for the discriminative power and the iterative termination. Finally,the feasibility and the effectiveness of the method are verified by several publicly available UCI and face data sets with promising results.

ADAPTIVE ESTIMATING DETAIL PRESERVING FILTER FOR IMAGE PROCESSING

A new adaptive detail preserving filter for image processing is presented.By comparing the difference of the values evaluated in the different directions or regions,this filter can decide which region (homogeneous region or detail region) the filtering pixels belong to and then apply different filtering schemes.This filter has better performance of noise filtering and detail preserving than the multistage median filter (MMF).It can be applied especially to the images simultaneously corrupted by Gaussian noise and impulsive noise,and is simple in computation and implementation.

HIGH RESOLUTION POSITIVITY-PRESERVING DIFFERENCE SCHEMES FOR TWO DIMENSIONAL EULER EQUATIONS

A class of high resolution positivity preserving Boltzmann type difference schemes for one and two dimensional Euler equations is studied. First, the relation between Boltzmann and Euler equations is analyzed. By using a kind of special interpolation, the high resolution Boltzmann type difference scheme is constructed. Finally, numerical tests show that the schemes are effective and useful.

Privacy preserving supply chain quantity discount contract design

The development and deployment of privary preserving supply chain quantity discount contract design can allow supply chain collaborations to take place without revealing any participant＇s data to others, reaping the benefits of collaborations wbile avoiding the drawbacks of privacy information disclosure. First, secure multi-party computation protocols are applied in the joint-ordering policy between a single supplier and a single retailer, the joint-ordering policy can be conducted without disclosing private cost information of any of the other supply chain partners. Secondly, secure multi-party computation protocols are applied in the privacy preserving supply chain quantity discount contract design between a single supplier and a single retailer. The information disclosure analyses of the algorithm show that： the optimal quantity discount of the jointordering policy can be conducted without disclosing private cost information of any of the other supply chain partners; the above protocol can be implemented without mediators; the privacy preserving quantity discount algorithm can be mutually verifiable and has solved the problem of asymmetric information.

DUAL-SPARSITY PRESERVING PROJECTION

Sparsity preserving projection（SPP） is a popular graph-based dimensionality reduction（DR） method, which has been successfully applied to solve face recognition recently. SPP contains natural discriminating information by preserving sparse reconstruction relationship of data sets. However, SPP suffers from the fact that every new feature learned from data sets is linear combinations of all the original features, which often makes it difficult to interpret the results. To address this issue, a novel DR method called dual-sparsity preserving projection （DSPP） is proposed to further impose sparsity constraints on the projection directions of SPP. Specifically, the proposed method casts the projection function learning of SPP into a regression-type optimization problem, and then the sparse projections can be efficiently computed by the related lasso algorithm. Experimental results from face databases demonstrate the effectiveness of the proposed algorithm.

Current status of function-preserving surgery for gastric cancer

Recent advances in diagnostic techniques have allowed the diagnosis of gastric cancer(GC)at an early stage.Due to the low incidence of lymph node metastasis and favorable prognosis in early GC,function-preserving surgery which improves postoperative quality of life may be possible.Pylorus-preserving gastrectomy(PPG)is one such function-preserving procedure,which is expected to offer advantages with regards to dumping syndrome,bile reflux gastritis,and the frequency of flatus,although PPG may induce delayed gastric emptying.Proximal gastrectomy(PG)is another functionpreserving procedure,which is thought to be advantageous in terms of decreased duodenogastric reflux and good food reservoir function in the remnant stomach,although the incidence of heartburn or gastric fullness associated with this procedure is high.However,these disadvantages may be overcome by the reconstruction method used.The other important problem after PG is remnant GC,which was reported to occur in approximately 5%of patients.Therefore,the reconstruction technique used with PG should facilitate postoperativeendoscopic examinations for early detection and treatment of remnant gastric carcinoma.Oncologic safety seems to be assured in both procedures,if the preoperative diagnosis is accurate.Patient selection should be carefully considered.Although many retrospective studies have demonstrated the utility of function-preserving surgery,no consensus on whether to adopt functionpreserving surgery as the standard of care has been reached.Further prospective randomized controlled trials are necessary to evaluate survival and postoperative quality of life associated with function-preserving surgery.

Huang's three-step maneuver for laparoscopic spleen-preserving No.10 lymph node dissection for advanced proximal gastric cancer

Due to the complexity of the splenic hilar vessels, their anatomical variation and the narrow and deep space, as well as the bleeding-prone splenic parenchyma and the difficulty to manage splenic or vascular bleeding at the splenic hilum,the procedure remains challenging and technically demanding procedure for the performance of laparoscopic pancreas- and spleen-preserving splenic hilar lymph nodes dissection. Based on our experiences, we gradually explored a set of procednral operation steps called ＂Huang＇s three-step maneuver＂. In this paper, we not only provide the concrete operation steps for the surgeon, but we also provide our recommended technique of pulling and exposure for assistants. This new maneuver simplifies the complicated procedure and improves the efficiency of laparoscopic spleen-preserving splenic hilar lymphadenectomy, making it easier to master and allowing for its widespread adoption.