Alignment Techniques in Total Knee Arthroplasty:Where do We Stand Today?

Achieving optimal alignment in total knee arthroplasty(TKA) is a critical factor in ensuring optimal outcomes and long-term implant survival. Traditionally, mechanical alignment has been favored to achieve neutral postoperative joint alignment. However, contemporary approaches, such as kinematic alignments and hybrid techniques including adjusted mechanical, restricted kinematic, inverse kinematic, and functional alignments, are gaining attention for their ability to restore native joint kinematics and anatomical alignment, potentially leading to enhanced functional outcomes and greater patient satisfaction. The ongoing debate on optimal alignment strategies considers the following factors: long-term implant durability, functional improvement, and resolution of individual anatomical variations. Furthermore, advancements of computer-navigated and robotic-assisted surgery have augmented the precision in implant positioning and objective measurements of soft tissue balance. Despite ongoing debates on balancing implant longevity and functional outcomes, there is an increasing advocacy for personalized alignment strategies that are tailored to individual anatomical variations. This review evaluates the spectrum of various alignment techniques in TKA, including mechanical alignment, patient-specific kinematic approaches, and emerging hybrid methods. Each technique is scrutinized based on its fundamental principles, procedural techniques, inherent advantages, and potential limitations, while identifying significant clinical gaps that underscore the need for further investigation.

Joint alignment and steering to manage interference

In wireless communication networks,mobile users in overlapping areas may experience severe interference,therefore,designing effective Interference Management(IM)methods is crucial to improving network performance.However,when managing multiple disturbances from the same source,it may not be feasible to use existing IM methods such as Interference Alignment(IA)and Interference Steering(IS)exclusively.It is because with IA,the aligned interference becomes indistinguishable at its desired Receiver(Rx)under the cost constraint of Degrees-of-Freedom(DoF),while with IS,more transmit power will be consumed in the direct and repeated application of IS to each interference.To remedy these deficiencies,Interference Alignment Steering(IAS)is proposed by incorporating IA and IS and exploiting their advantages in IM.With IAS,the interfering Transmitter(Tx)first aligns one interference incurred by the transmission of one data stream to a one-dimensional subspace orthogonal to the desired transmission at the interfered Rx,and then the remaining interferences are treated as a whole and steered to the same subspace as the aligned interference.Moreover,two improved versions of IAS,i.e.,IAS with Full Adjustment at the Interfering Tx(IAS-FAIT)and Interference Steering and Alignment(ISA),are presented.The former considers the influence of IA on the interfering user-pair's performance.The orthogonality between the desired signals at the interfered Rx can be maintained by adjusting the spatial characteristics of all interferences and the aligned interference components,thus ensuring the Spectral Efficiency(SE)of the interfering communication pairs.Under ISA,the power cost for IS at the interfered Tx is minimized,hence improving SE performance of the interfered communication-pairs.Since the proposed methods are realized at the interfering and interfered Txs cooperatively,the expenses of IM are shared by both communication-pairs.Our in-depth simulation results show that joint use of IA and IS can effectively manage multiple disturbances from the same source and improve the system's SE.

Optimizing Deep Learning for Computer-Aided Diagnosis of Lung Diseases: An Automated Method Combining Evolutionary Algorithm, Transfer Learning, and Model Compression

Recent developments in Computer Vision have presented novel opportunities to tackle complex healthcare issues,particularly in the field of lung disease diagnosis.One promising avenue involves the use of chest X-Rays,which are commonly utilized in radiology.To fully exploit their potential,researchers have suggested utilizing deep learning methods to construct computer-aided diagnostic systems.However,constructing and compressing these systems presents a significant challenge,as it relies heavily on the expertise of data scientists.To tackle this issue,we propose an automated approach that utilizes an evolutionary algorithm(EA)to optimize the design and compression of a convolutional neural network(CNN)for X-Ray image classification.Our approach accurately classifies radiography images and detects potential chest abnormalities and infections,including COVID-19.Furthermore,our approach incorporates transfer learning,where a pre-trainedCNNmodel on a vast dataset of chest X-Ray images is fine-tuned for the specific task of detecting COVID-19.This method can help reduce the amount of labeled data required for the task and enhance the overall performance of the model.We have validated our method via a series of experiments against state-of-the-art architectures.

Improved spatio-temporal alignment measurement method for hull deformation

In this paper,an improved spatio-temporal alignment measurement method is presented to address the inertial matching measurement of hull deformation under the coexistence of time delay and large misalignment angle.Large misalignment angle and time delay often occur simultaneously and bring great challenges to the accurate measurement of hull deformation in space and time.The proposed method utilizes coarse alignment with large misalignment angle and time delay estimation of inertial measurement unit modeling to establish a brand-new spatiotemporal aligned hull deformation measurement model.In addition,two-step loop control is designed to ensure the accurate description of dynamic deformation angle and static deformation angle by the time-space alignment method of hull deformation.The experiments illustrate that the proposed method can effectively measure the hull deformation angle when time delay and large misalignment angle coexist.

Beam based alignment using a neural network

Beams typically do not travel through the magnet centers because of errors in storage rings.The beam deviating from the quadrupole centers is affected by additional dipole fields due to magnetic field feed-down.Beam-based alignment(BBA)is often performed to determine a golden orbit where the beam circulates around the quadrupole center axes.For storage rings with many quadrupoles,the conventional BBA procedure is time-consuming,particularly in the commissioning phase,because of the necessary iterative process.In addition,the conventional BBA method can be affected by strong coupling and the nonlinearity of the storage ring optics.In this study,a novel method based on a neural network was proposed to determine the golden orbit in a much shorter time with reasonable accuracy.This golden orbit can be used directly for operation or adopted as a starting point for conventional BBA.The method was demonstrated in the HLS-II storage ring for the first time through simulations and online experiments.The results of the experiments showed that the golden orbit obtained using this new method was consistent with that obtained using the conventional BBA.The development of this new method and the corresponding experiments are reported in this paper.

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.

Automatic Fetal Segmentation Designed on Computer-Aided Detection with Ultrasound Images

In the present research,we describe a computer-aided detection(CAD)method aimed at automatic fetal head circumference(HC)measurement in 2D ultrasonography pictures during all trimesters of pregnancy.The HC might be utilized toward determining gestational age and tracking fetal development.This automated approach is particularly valuable in low-resource settings where access to trained sonographers is limited.The CAD system is divided into two steps:to begin,Haar-like characteristics were extracted from ultrasound pictures in order to train a classifier using random forests to find the fetal skull.We identified the HC using dynamic programming,an elliptical fit,and a Hough transform.The computer-aided detection(CAD)program was well-trained on 999 pictures(HC18 challenge data source),and then verified on 335 photos from all trimesters in an independent test set.A skilled sonographer and an expert in medicine personally marked the test set.We used the crown-rump length(CRL)measurement to calculate the reference gestational age(GA).In the first,second,and third trimesters,the median difference between the standard GA and the GA calculated by the skilled sonographer stayed at 0.7±2.7,0.0±4.5,and 2.0±12.0 days,respectively.The regular duration variance between the baseline GA and the health investigator’s GA remained 1.5±3.0,1.9±5.0,and 4.0±14 a couple of days.The mean variance between the standard GA and the CAD system’s GA remained between 0.5 and 5.0,with an additional variation of 2.9 to 12.5 days.The outcomes reveal that the computer-aided detection(CAD)program outperforms an expert sonographer.When paired with the classifications reported in the literature,the provided system achieves results that are comparable or even better.We have assessed and scheduled this computerized approach for HC evaluation,which includes information from all trimesters of gestation.

On The Cauchy Problem for A 1D Euler-Alignment System in Besov Spaces

In this paper, we investigate a 1D pressureless Euler-alignment system with a non-local alignment term, describing a kind of self-organizing problem for flocking. As a result, by the transport equation theory and Lagrange coordinate transformation, the local well-posedness of the solutions for the 1D pressureless Euler-alignment in Besov spaces with 1≤p∞ is established. Next, the ill-posedness of the solutions for this model in Besov spaces with 1≤p and is also deduced. Finally, the precise blow-up criteria of the solutions for this system is presented in Besov spaces with 1≤p .

Initial Alignment Technique for SINS of Vehicles in the Moving State

An initial alignment technique for the strapdown inertial navigation system (SINS) of vehicles in the moving state is researched. By selecting an odometer as the system’s external sensor, the mathematical model for the alignment in the moving state is established and the observability of the system is analyzed. The results show that the SINS can successfully achieve the precision alignment in 10 min when the vehicle is moving toward the prearranged place after its staying for several seconds to perform the coarse alignment. The precision of alignment can also be improved in the moving state compared with that in the static state.

由一般拓扑度量空间所产生的Alignment空间

Alignment空间是一个在广义误差下定义的度量空间。在以往的信息处理问题中,一般只讨论离散状态下的序列比对Alignment问题,并由此产生一种新的非线性度量空间-Alignment空间。本文将离散状态下的Alignment空间推广到一般情况,得到了由一般拓扑度量空间所产生的Alignment空间仍然是度量空间,并证明了Alignment距离与Levenshtein距离的等价性。

Ground Alignment of Inertial Navigation System with the Aid of GPS Signals

Due to the poor observability of INS ground self alignment, only horizontal alignment is satisfied. This paper proposes using GPS double difference carrier phase as external reference to improve the observability of INS self alignment. Through observability analysis and computer simulation, it is demonstrated that the azimuth alignment is as quick as horizontal alignment, the accuracy of horizontal alignment is improved, and the gyros errors can be estimated quickly and precisely.

Ultra-precision alignment technique based on modified Moiré signals

A novel method for automatic ultra-precision alignment is presented.This method relies on the modified Moiré technique,and alignment marks are used in the form of gratings.The modified Moiré technique can effectively improve detecting sensitivity of signals and simplify the control system by using only one pair of laser-Moiré sensors.We present the mathematical model and simulation results of diffracting two gratings.The effect of various parameters on Moiré signals is studied theoretically and experimentally,and the results are found to be consistent.A computer controlled alignment device using one pair of Moiré sensors is designed.The device can achieve a fully automatic precision alignment by the modified Moiré signal.The experimental result shows that the alignment device can obtain the resolution of 5 nm and the positioning accuracy of ±0 5 μm.

Robust UKF algorithm in SINS initial alignment

In the traditional unscented Kalman filter（UKF）,accuracy and robustness decline when uncertain disturbances exist in the practical system.To deal with the problem,a robust UKF algorithm based on an H-infinity norm is proposed.In Krein space,a robust element is added in the simplified UKF so as to improve the algorithm.The filtering gain is adjusted by the robust element and in this way the performance of the robustness of the filtering algorithm is promoted.In the initial alignment process of the large heading misalignment angle of the strapdown inertial navigation system（SINS）,comparative studies are conducted on the robust UKF and the simplified UKF.The simulation results illustrate that compared with the simplified UKF,the robust UKF is more accurate,and the estimation error of heading misalignment decreases from 16.9＇ to 4.3＇.In short,the robust UKF can reduce the sensitivity to the system disturbances resulting in better performance.

Vector Correlations and Product Rotational Alignments of Reactions Ca+RBr→CaBr+R(R=CH_(3),C_(2) H_(5) and n-C_(3) H_(7))

The quasiclassical trajectory method is used to study the vector correlations of the reactions Ca＋RBr （R=CH3, C2H5 and n-C3H7Br） and the rotational alignment of product CaBr. The product rotational alignment parameters at different collision energies and the vector correlations between the reagent and product are numerically calculated. The vector correlations are described by using the angle distribution functions P（θr）, P（φr）, P（θr, φr） and the polarization-dependent differential cross sections （PDDCSs）. The peak values of P（θr） of the product CaBr from Ca＋CH3Br are larger than those from Ca＋C2H5Br and Ca＋n-C3H7Br. The peak of P（θr） at φr= 3π/2 is apparently stronger than that at φr= x/2 for the three reactions Ca＋RBr. The calculation results show that the rotational angular momentum of the product CaBr is not only aligned, but also oriented along the direction which is perpendicular to the scattering plane.The product CaBr molecules are strongly scattered forward. The orientation and alignment of the product angular momentum will affect the scattering direction of the product molecules to varying degrees.

归一化Alignment距离

为了更好地度量不同序列之间的差异,本文在给定度量空间上的符号距离满足特定条件的前提下,以Alignment距离为基础,提出了一种归一化Alignment距离.接下来利用Alignment相关度证明了该距离满足度量定义的三个条件,并且取值在[0,1]区间上.最后对其进行了进一步讨论,从理论上说明该定义的合理性.该距离可以在序列比对、聚类分析以及模式识别等领域中发挥重要作用.

Reference information time delay estimation and compensation in transfer alignment

To reduce the error in transfer alignment caused by reference information delay,a time delay estimation method is developed based on least-squares curve fitting of the angular rate integration.First,the gyro sensor measurements of the main strapdown inertial navigation system（M-SINS） and the slave strapdown inertial navigation system（S-SINS） are recorded for a few seconds and the integration of the data is calculated.Then,the possible maximum range of the delay value is defined and the points of the curve at different intervals are moved.The square of the differences between the corresponding points are calculated.Finally,the delay estimation can be acquired by the least-squares curve fitting of the M-SINS and the S-SINS.A delay compensation method by local data shifting is also presented.The simulation results demonstrate the effectiveness of delay estimation and indicate that the estimation accuracy is independent of the delay value.And the local data shifting compensation method can effectively reduce the errors of the transfer alignment caused by the reference information delay.

Alignment空间的几点注记

Alignment空间是一种由度量空间产生的度量空间,其中的度量函数称为Alignment距离.本文首先利用扩张结构的方法直接证明了Alignment距离是度量空间中的度量函数.接下来讨论了Alignment空间中逆序列和子序列的Alignment距离之间的关系,给出了关于Alignment距离的一系列不等式.

利用Alignment空间理论分析蛋白质的结构

蛋白质的结构分析对研究蛋白质的功能以及进化关系十分重要,而Alignment空间理论作为数学中一个崭新的方向,可以在蛋白质结构分析中发挥显著的作用。简要介绍了Alignment空间理论,采用基于蛋白质主链的二面角序列代表蛋白质的结构,利用Alignment空间理论,通过相应的动态规划算法得到了一种可以度量蛋白质结构相似程度的指标ρ。最后经由蛋白质序列的实例计算证明该指标的有效性。

Alignment空间及其虚拟符号运算

本文提出Alignment空间中虚拟符号运算的概念,并且给出一系列关于虚拟符号运算处理序列的性质.

Alignment空间中的一个计数结果

Alignment空间是一种由广义差错产生的度量空间,其中的序列结构分析在应用领域中起到了越来越重要的作用.本文主要对Alignment空间F2*中Alignment距离为2的序列结构进行了研究,提出Alignment距离分布的概念,得到了长度为n并且Alignment距离为2的序列对数目的解析表达式.