Laparoscopic hepatectomy using indocyanine green attenuates postoperative inflammatory response for hepatocellular carcinoma:A propensity score matching analysis

BACKGROUND Improving the intraoperative and postoperative performance of laparoscopic hepatectomy was quite a challenge for liver surgeons.AIM To determine the benefits of indocyanine green(ICG)fluorescence imaging in patients with hepatocellular carcinoma(HCC)who underwent laparoscopic hepatectomy during and after surgery.METHODS We retrospectively collected the clinicopathological data of 107 patients who successfully underwent laparoscopic hepatectomy at Zhongshan Hospital(Xiamen),Fudan University from June 2022 to June 2023.Whether using the ICG fluorescence imaging technique,we divided them into the ICG and non-ICG groups.To eliminate statistical bias,a 1:1 propensity score matching analysis was conducted.The comparison of perioperative outcomes,including inflammationrelated markers and progression-free survival,was analyzed statistically.RESULTS Intraoperatively,the ICG group exhibited lower blood loss,a shorter surgical time,lower hepatic inflow occlusion(HIO)frequency,and a shorter total HIO time.Postoperatively,the participation of ICG resulted in a shorter duration of hospitalization(6.5 vs 7.6 days,P=0.03)and postoperative inflammatory response attenuation(lower neutrophil-lymphocyte ratio on the first day after surgery and platelet-lymphocyte ratio on the third day,P<0.05).Although the differences were not significant,the levels of all inflammation-related markers were lower in the ICG group.The rates of postoperative complications and the survival analyses,including progression-free and overall survivals showed no significant difference between the groups.CONCLUSION The involvement of ICG fluorescence imaging may lead to improved perioperative outcomes,especially postoperative inflammatory response attenuation,and ultimately improve HCC patients’recovery after surgery.

Efficacy-cost analysis of endoscopic mucosal resection and cold snare polypectomy:A propensity score matching analysis

BACKGROUND Although substantial evidence supports the advantages of cold snare polypectomy(CSP)in terms of polypectomy efficacy and reduced postoperative adverse events,few studies have examined the cost differences between CSP and traditional endoscopic mucosal resection(EMR)for the treatment of intestinal polyps.AIM To compare the efficacy-cost of EMR and CSP in the treatment of intestinal polyps.METHODS A total of 100 patients with intestinal polyps were included in the retrospective data of our hospital from April 2022 to May 2023.According to the treatment methods,they were divided into EMR(n=46)group and CSP(n=54)group.The baseline data of the two groups were balanced by 1:1 propensity score matching(PSM),and the cost-effectiveness analysis was performed on the two groups after matching.The recurrence rate of the two groups of patients was followed up for 1 year,and they were divided into recurrence group and non-recurrence group according to whether they recurred.Multivariate logistic regression analysis was used to screen out the influencing factors affecting the recurrence of intestinal polyps after endoscopic resection.RESULTS Significant disparities were observed in the number of polyps and smoking background between the two groups before PSM(P<0.05).Following PSM,the number of polyps and smoking history were well balanced between the EMR and CSP groups.The direct cost incurred by the CSP group was markedly higher than that incurred by the EMR group.Concurrently,the cost-effectiveness ratio in the CSP group was substantially reduced when juxtaposed with that in the EMR group(P<0.05).Upon completion of the 1-year follow-up,the rate of recurrence after endoscopic intestinal polypectomy was 38.00%.Multivariate methods revealed that age≥60 years,male sex,number of polyps≥3,and pathological type of adenoma were risk factors for recurrence after endoscopic intestinal polypectomy(all P<0.05).CONCLUSION CSP was more cost-effective for the treatment of intestinal polyps.An age≥60 years,male sex,having a number of polyps≥3,and pathological type of adenoma are independent influencing factors for recurrence.

A new three-phase 5-level current-source inverter

The new three-phase 5-level current-source inverter (CSI) proposed in this paper was developed by connecting three separate single-phase 5-level CSIs in series, and its operational principle was analyzed. There are two major problems existing in current-source multilevel inverters, one is the complex PWM control method (2-logic to 3-logic conversion), and the other is the problem of current-unbalance between different levels. A simple current-balance control method via DC current feedback is applied in each single-phase 5-level CSI cell to implement the current-balance control between different levels. And to reduce the output current harmonics, POD PWM control technique was used. Simulation and experimental results showed that this new three-phase 5-level CSI topology operates correctly.

A 6-switch single-phase 5-level current-source inverter

The new 6-switch single-phase 5-level current-source inverter proposed in this paper was developed by properly simplifying the traditional 8-switch single-phase 5-level current-source inverter, and its operational principle was analyzed. Just like the problem of voltage-unbalance between different levels existing in voltage-source multilevel inverters, a similar problem of current-unbalance between different levels whether for the 8-switch single-phase 5-level current-source inverter, or for the new 6-switch 5-level current-source inverter also exists. A simple current-balance control method via DC current feedback is presented here to implement the current-balance control between different levels. And to reduce the output current harmonics, PWM control technique was used. Simulation and experimental results showed that this new 6-switch topology operates correctly and that the balance-inductor can almost equally distribute the total DC current.

Novel control scheme for 3-phase PWM current-source converters under unbalanced source voltage conditions

Under unbalanced source voltage supply, considerable output second harmonics and input low-order harmonics in 3-phase PWM current-source converters (PWM-CSC) are generated. This paper proposes a new deadbeat controller based on compensation for unbalanced source voltage and current. With the proposed scheme, the second harmonics of the output current are eliminated and low-order harmonics of the source current are reduced effectively. Simulation and experimental results con- firmed the feasibility of the proposed method.

Topology for current-source grid-connected inverter with improved effciency and pulse width modulation control strategy

A novel topology of the current-source grid-connected inverter is proposed based on the immittance converter theory. A control strategy of sine-sine pulse width modulation （PWM） is studied. Compared with the traditional current-source inverter, the power frequency inductors and power frequency transformer are replaced with high frequency inductors and a high frequency transformer. Thus, the proposed inverter has advantages of small volume, low cost, low total harmonic distortion （THD）, low power losses, high power factor （PF） and simple control. Furthermore, grid voltage cannot influence output current of the grid-connected inverter and the current-source inverter with a high PF that approaches one has been realized. Finally, validity of the theory analysis and feasibility of the control scheme are shown by simulation and experimental results.

Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

Road traffic monitoring is an imperative topic widely discussed among researchers.Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides.However,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area.To this end,different models have shown the ability to recognize and track vehicles.However,these methods are not mature enough to produce accurate results in complex road scenes.Therefore,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image bursts.The extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the images.The masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring system.Next,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise reduction.After preprocessing,the blob detection algorithm helped detect the vehicles.Vehicles of varying sizes have been detected by implementing a dynamic thresholding scheme.Detection was done on the first image of every burst.Then,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching algorithm.To further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple matches.An accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been achieved.For the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

A Portfolio Selection Method Based on Pattern Matching with Dual Information of Direction and Distance

Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of similarity sets, and proposes a Portfolio Selection Method based on Pattern Matching with Dual Information of Direction and Distance (PMDI). By studying different combination methods of indicators such as Euclidean distance, Chebyshev distance, and correlation coefficient, important information such as direction and distance in stock historical price information is extracted, thereby filtering out the similarity set required for pattern matching based investment portfolio selection algorithms. A large number of experiments conducted on two datasets of real stock markets have shown that PMDI outperforms other algorithms in balancing income and risk. Therefore, it is suitable for the financial environment in the real world.

Feature Matching via Topology-Aware Graph Interaction Model

Feature matching plays a key role in computer vision. However, due to the limitations of the descriptors, the putative matches are inevitably contaminated by massive outliers.This paper attempts to tackle the outlier filtering problem from two aspects. First, a robust and efficient graph interaction model,is proposed, with the assumption that matches are correlated with each other rather than independently distributed. To this end, we construct a graph based on the local relationships of matches and formulate the outlier filtering task as a binary labeling energy minimization problem, where the pairwise term encodes the interaction between matches. We further show that this formulation can be solved globally by graph cut algorithm. Our new formulation always improves the performance of previous localitybased method without noticeable deterioration in processing time,adding a few milliseconds. Second, to construct a better graph structure, a robust and geometrically meaningful topology-aware relationship is developed to capture the topology relationship between matches. The two components in sum lead to topology interaction matching(TIM), an effective and efficient method for outlier filtering. Extensive experiments on several large and diverse datasets for multiple vision tasks including general feature matching, as well as relative pose estimation, homography and fundamental matrix estimation, loop-closure detection, and multi-modal image matching, demonstrate that our TIM is more competitive than current state-of-the-art methods, in terms of generality, efficiency, and effectiveness. The source code is publicly available at http://github.com/YifanLu2000/TIM.

Multiple Matching Attenuation Based on Curvelet Domain Extended Filtering

The paper develops a multiple matching attenuation method based on extended filtering in the curvelet domain,which combines the traditional Wiener filtering method with the matching attenuation method in curvelet domain.Firstly,the method uses the predicted multiple data to generate the Hilbert transform records,time derivative records and time derivative records of Hilbert transform.Then,the above records are transformed into the curvelet domain and multiple matching attenuation based on least squares extended filtering is performed.Finally,the attenuation results are transformed back into the time-space domain.Tests on the model data and field data show that the method proposed in the paper effectively suppress the multiples while preserving the primaries well.Furthermore,it has higher accuracy in eliminating multiple reflections,which is more suitable for the multiple attenuation tasks in the areas with complex structures compared to the time-space domain extended filtering method and the conventional curvelet transform method.

Perfect 1-k Matchings of Bipartite Graphs

Let k be a positive integer and G a bipartite graph with bipartition (X,Y). A perfect 1-k matching is an edge subset M of G such that each vertex in Y is incident with exactly one edge in M and each vertex in X is incident with exactly k edges in M. A perfect 1-k matching is an optimal semi-matching related to the load-balancing problem, where a semi-matching is an edge subset M such that each vertex in Y is incident with exactly one edge in M, and a vertex in X can be incident with an arbitrary number of edges in M. In this paper, we give three sufficient and necessary conditions for the existence of perfect 1-k matchings and for the existence of 1-k matchings covering | X |−dvertices in X, respectively, and characterize k-elementary bipartite graph which is a graph such that the subgraph induced by all k-allowed edges is connected, where an edge is k-allowed if it is contained in a perfect 1-k matching.

A Non-Parametric Scheme for Identifying Data Characteristic Based on Curve Similarity Matching

For accurately identifying the distribution charac-teristic of Gaussian-like noises in unmanned aerial vehicle(UAV)state estimation,this paper proposes a non-parametric scheme based on curve similarity matching.In the framework of the pro-posed scheme,a Parzen window(kernel density estimation,KDE)method on sliding window technology is applied for roughly esti-mating the sample probability density,a precise data probability density function(PDF)model is constructed with the least square method on K-fold cross validation,and the testing result based on evaluation method is obtained based on some data characteristic analyses of curve shape,abruptness and symmetry.Some com-parison simulations with classical methods and UAV flight exper-iment shows that the proposed scheme has higher recognition accuracy than classical methods for some kinds of Gaussian-like data,which provides better reference for the design of Kalman filter(KF)in complex water environment.

Distributed Matching Theory-Based Task Re-Allocating for Heterogeneous Multi-UAV Edge Computing

Many efforts have been devoted to efficient task scheduling in Multi-Unmanned Aerial Vehicle(UAV)edge computing.However,the heterogeneity of UAV computation resource,and the task re-allocating between UAVs have not been fully considered yet.Moreover,most existing works neglect the fact that a task can only be executed on the UAV equipped with its desired service function(SF).In this backdrop,this paper formulates the task scheduling problem as a multi-objective task scheduling problem,which aims at maximizing the task execution success ratio while minimizing the average weighted sum of all tasks’completion time and energy consumption.Optimizing three coupled goals in a realtime manner with the dynamic arrival of tasks hinders us from adopting existing methods,like machine learning-based solutions that require a long training time and tremendous pre-knowledge about the task arrival process,or heuristic-based ones that usually incur a long decision-making time.To tackle this problem in a distributed manner,we establish a matching theory framework,in which three conflicting goals are treated as the preferences of tasks,SFs and UAVs.Then,a Distributed Matching Theory-based Re-allocating(DiMaToRe)algorithm is put forward.We formally proved that a stable matching can be achieved by our proposal.Extensive simulation results show that Di Ma To Re algorithm outperforms benchmark algorithms under diverse parameter settings and has good robustness.

Heparin-binding protein as a predictor of mortality in patients with diabetes mellitus and community-acquired pneumonia in intensive care unit:a propensity score matched study

BACKGROUND:Patients with diabetes mellitus(DM)are vulnerable to community-acquired pneumonia(CAP),which have a high mortality rate.We aimed to investigate the value of heparin-binding protein(HBP)as a prognostic marker of mortality in patients with DM and CAP.METHODS:This retrospective study included CAP patients who were tested for HBP at intensive care unit(ICU)admission from January 2019 to April 2020.Patients were allocated to the DM or non-DM group and paired with propensity score matching.Baseline characteristics and clinical outcomes up to 90 days were evaluated.The primary outcome was the 10-day mortality.Receiver operating characteristic(ROC)curves,Kaplan-Meier analysis,and Cox regression were used for statistical analysis.RESULTS:Among 152 enrolled patients,60 pairs were successfully matched.There was no significant difference in 10-day mortality,while more patients in the DM group died within 28 d(P=0.024)and 90 d(P=0.008).In the DM group,HBP levels at ICU admission were higher in 10-day non-survivors than in 10-day survivors(median 182.21[IQR:55.43-300]ng/ml vs.median 66.40[IQR:34.13-107.85]ng/mL,P=0.019),and HBP levels could predict the 10-day mortality with an area under the ROC curve of 0.747.The cut-off value,sensitivity,and specificity were 160.6 ng/mL,66.7%,and 90.2%,respectively.Multivariate Cox regression analysis indicated that HBP was an independent prognostic factor for 10-day(HR 7.196,95%CI:1.596-32.455,P=0.01),28-day(HR 4.381,95%CI:1.449-13.245,P=0.009),and 90-day mortality(HR 4.581,95%CI:1.637-12.819,P=0.004)in patients with DM.CONCLUSION:Plasma HBP at ICU admission was associated with the 10-day,28-day,and 90-day mortality,and might be a prognostic factor in patients with DM and CAP.

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

CMMCAN:Lightweight Feature Extraction and Matching Network for Endoscopic Images Based on Adaptive Attention

In minimally invasive surgery,endoscopes or laparoscopes equipped with miniature cameras and tools are used to enter the human body for therapeutic purposes through small incisions or natural cavities.However,in clinical operating environments,endoscopic images often suffer from challenges such as low texture,uneven illumination,and non-rigid structures,which affect feature observation and extraction.This can severely impact surgical navigation or clinical diagnosis due to missing feature points in endoscopic images,leading to treatment and postoperative recovery issues for patients.To address these challenges,this paper introduces,for the first time,a Cross-Channel Multi-Modal Adaptive Spatial Feature Fusion(ASFF)module based on the lightweight architecture of EfficientViT.Additionally,a novel lightweight feature extraction and matching network based on attention mechanism is proposed.This network dynamically adjusts attention weights for cross-modal information from grayscale images and optical flow images through a dual-branch Siamese network.It extracts static and dynamic information features ranging from low-level to high-level,and from local to global,ensuring robust feature extraction across different widths,noise levels,and blur scenarios.Global and local matching are performed through a multi-level cascaded attention mechanism,with cross-channel attention introduced to simultaneously extract low-level and high-level features.Extensive ablation experiments and comparative studies are conducted on the HyperKvasir,EAD,M2caiSeg,CVC-ClinicDB,and UCL synthetic datasets.Experimental results demonstrate that the proposed network improves upon the baseline EfficientViT-B3 model by 75.4%in accuracy(Acc),while also enhancing runtime performance and storage efficiency.When compared with the complex DenseDescriptor feature extraction network,the difference in Acc is less than 7.22%,and IoU calculation results on specific datasets outperform complex dense models.Furthermore,this method increases the F1 score by 33.2%and accelerates runtime by 70.2%.It is noteworthy that the speed of CMMCAN surpasses that of comparative lightweight models,with feature extraction and matching performance comparable to existing complex models but with faster speed and higher cost-effectiveness.

3D electromagnetic simulation of the coupling characteristics and double-stub Ferrite tuners impedance matching for EAST ICRH four-strap antenna

A program developed with COMSOL software integrates EAST four-strap antenna coupling with the double-stub Ferrite tuners(FT)impedance matching,obtaining physical quantities crucial for predicting the overall performance of the ion cyclotron resonance heating(ICRH)antenna and matching system.These quantities encompass S-matrix,port complex impedance,reflection coefficients,electric field and voltage distribution,and optimal matching settings.In this study,we explore the relationship between S-matrix,reflection coefficients,port complex impedance,and frequency.Then,we analyze the impact of Faraday screens placement position and transparency,the distance from the Faraday screen(FS)to the current straps(CS),the relative distance between ports,and the characteristic impedance of the transmission line on the coupling characteristic impedance of the EAST ICRH system.Finally,we simulate the electric field distribution and voltage distribution of the EAST ICRH system for plasma heating with double-stub FT impedance matching.Using optimized parameters,the coupling power of the ICRH system can be approximately doubled.The results present herein may offer guidance for the design of high-power,long-pulse operation ICRH antenna systems.

Facilely constructing ultrahigh lattice-matched CuZn_(5) epitaxial interface for dendrite-free Zn metal anode

Constructing a protective layer on Zn anode surface with high lattice matching to Zn(002)can facilitate preferential growth along the(002)crystal plane and suppress dendritic growth as well as interface side reactions.Whereas most of protective layers are complex and costly,making commercial applications challenging.Herein,we introduce a facile method involving the addition of CuCl_(2) electrolyte additives to conventional electrolyte systems,which,through rapid displacement reactions and controlled electrochemical cycling,forms a CuZn_(5) alloy layer with 97.2%lattice matching to the(002)plane(CuZn_(5)@Zn),thus regulating the(002)plane epitaxial deposition.As a result,the symmetric cells with CuZn_(5)@Zn demonstrate an ultra-long cycle life of 3600 h at 1 mA cm^(-2).Under extreme conditions of high current density(20 m A cm^(-2))and high zinc utilization(DOD_(Zn)=50%),stable cycling performance is maintained for 220 and 350 h,respectively.Furthermore,the CuZn_(5)@Zn||NH_(4)V_(4)O_(10)full cell maintains a capacity of 120 m A h g^(-1)even after 10,000 cycles at a high current density of 10 A g^(-1).This work presents a facile and efficient strategy for constructing stable metal anode materials,with implications for the development of next-generation rechargeable batteries.

Artificial Immune Detection for Network Intrusion Data Based on Quantitative Matching Method

Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods.This paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching method.The proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune elements.Then,to improve the accuracy of similarity calculation,a quantitative matching method is proposed.The model uses mathematical methods to train and evolve immune elements,increasing the diversity of immune recognition and allowing for the successful detection of unknown intrusions.The proposed model’s objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection,overcoming the disadvantages of traditional methods.The experiment results show that the proposed model can detect intrusions effectively.It has a detection rate of more than 99.6%on average and a false alarm rate of 0.0264%.It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance.

Reshaping Li–Mg hybrid batteries:Epitaxial electrodeposition and spatial confinement on MgMOF substrates via the lattice‐matching strategy

The emergence of Li–Mg hybrid batteries has been receiving attention,owing to their enhanced electrochemical kinetics and reduced overpotential.Nevertheless,the persistent challenge of uneven Mg electrodeposition remains a significant impediment to their practical integration.Herein,we developed an ingenious approach that centered around epitaxial electrocrystallization and meticulously controlled growth of magnesium crystals on a specialized MgMOF substrate.The chosen MgMOF substrate demonstrated a robust affinity for magnesium and showed minimal lattice misfit with Mg,establishing the crucial prerequisites for successful heteroepitaxial electrocrystallization.Moreover,the incorporation of periodic electric fields and successive nanochannels within the MgMOF structure created a spatially confined environment that considerably promoted uniform magnesium nucleation at the molecular scale.Taking inspiration from the“blockchain”concept prevalent in the realm of big data,we seamlessly integrated a conductive polypyrrole framework,acting as a connecting“chain,”to interlink the“blocks”comprising the MgMOF cavities.This innovative design significantly amplified charge‐transfer efficiency,thereby increasing overall electrochemical kinetics.The resulting architecture(MgMOF@PPy@CC)served as an exceptional host for heteroepitaxial Mg electrodeposition,showcasing remarkable electrostripping/plating kinetics and excellent cycling performance.Surprisingly,a symmetrical cell incorporating the MgMOF@PPy@CC electrode demonstrated impressive stability even under ultrahigh current density conditions(10mAcm^(–2)),maintaining operation for an extended 1200 h,surpassing previously reported benchmarks.Significantly,on coupling the MgMOF@PPy@CC anode with a Mo_(6)S_(8) cathode,the assembled battery showed an extended lifespan of 10,000 cycles at 70 C,with an outstanding capacity retention of 96.23%.This study provides a fresh perspective on the rational design of epitaxial electrocrystallization driven by metal–organic framework(MOF)substrates,paving the way toward the advancement of cuttingedge batteries.