Establishing minimum clinically important difference values for the Patient-Reported Outcomes Measurement Information System Physical Function, hip disability and osteoarthritis outcome score for joint reconstruction, and knee injury and osteoarthritis out

AIM To establish minimum clinically important difference(MCID) for measurements in an orthopaedic patient population with joint disorders.METHODS Adult patients aged 18 years and older seeking care for joint conditions at an orthopaedic clinic took the Patient-Reported Outcomes Measurement Information System Physical Function(PROMIS~? PF) computerized adaptive test(CAT), hip disability and osteoarthritis outcome score for joint reconstruction(HOOS JR), and the knee injury and osteoarthritis outcome score for joint reconstruction(KOOS JR) from February 2014 to April 2017. MCIDs were calculated using anchorbased and distribution-based methods. Patient reports of meaningful change in function since their first clinic encounter were used as an anchor.RESULTS There were 2226 patients who participated with a mean age of 61.16(SD = 12.84) years, 41.6% male, and 89.7% Caucasian. Mean change ranged from 7.29 to 8.41 for the PROMIS~? PF CAT, from 14.81 to 19.68 for the HOOS JR, and from 14.51 to 18.85 for the KOOS JR. ROC cut-offs ranged from 1.97-8.18 for the PF CAT, 6.33-43.36 for the HOOS JR, and 2.21-8.16 for the KOOS JR. Distribution-based methods estimated MCID values ranging from 2.45 to 21.55 for the PROMIS~? PF CAT; from 3.90 to 43.61 for the HOOS JR, and from 3.98 to 40.67 for the KOOS JR. The median MCID value in the range was similar to the mean change score for each measure and was 7.9 for the PF CAT, 18.0 for the HOOS JR, and 15.1 for the KOOS JR.CONCLUSION This is the first comprehensive study providing a wide range of MCIDs for the PROMIS? PF, HOOS JR, and KOOS JR in orthopaedic patients with joint ailments.

Kinematic Research on Hinged Synchronous Universal Joint Applied for Snake-like Manipulator

The structure and motion principle of a hinged synchronous universal joint (HSUJ) is introduced, also whose kinematics is theoretically analyzed. As a result, a few kinematic characters of the HSUJ are obtained,which establish the foundation of its application for snake-like manipulator. Making use of the HSUJ ss actuating mechauism, the developed snake-like manipulator has the merits of small curve radius, fewer actuator, and small volume etc.

Low-Complexity Joint Channel Estimation and Symbol Detection for OFDMA Systems

In this paper,we propose a joint channel estimation and symbol detection(JCESD)algorithm relying on message-passing algorithms(MPA)for orthogonal frequency division multiple access(OFDMA)systems.The channel estimation and symbol detection leverage the framework of expectation propagation(EP)and belief propagation(BP)with the aid of Gaussian approximation,respectively.Furthermore,to reduce the computation complexity involved in channel estimation,the matrix inversion is transformed into a series of diagonal matrix inversions through the Sherman-Morrison formula.Simulation experiments show that the proposed algorithm can reduce the pilot overhead by about 50%,compared with the traditional linear minimum mean square error(LMMSE)algorithm,and can approach to the bit error rate(BER)performance bound of perfectly known channel state information within 0.1 dB.

Industrial Fusion Cascade Detection of Solder Joint

With the remarkable advancements in machine vision research and its ever-expanding applications,scholars have increasingly focused on harnessing various vision methodologies within the industrial realm.Specifically,detecting vehicle floor welding points poses unique challenges,including high operational costs and limited portability in practical settings.To address these challenges,this paper innovatively integrates template matching and the Faster RCNN algorithm,presenting an industrial fusion cascaded solder joint detection algorithm that seamlessly blends template matching with deep learning techniques.This algorithm meticulously weights and fuses the optimized features of both methodologies,enhancing the overall detection capabilities.Furthermore,it introduces an optimized multi-scale and multi-template matching approach,leveraging a diverse array of templates and image pyramid algorithms to bolster the accuracy and resilience of object detection.By integrating deep learning algorithms with this multi-scale and multi-template matching strategy,the cascaded target matching algorithm effectively accurately identifies solder joint types and positions.A comprehensive welding point dataset,labeled by experts specifically for vehicle detection,was constructed based on images from authentic industrial environments to validate the algorithm’s performance.Experiments demonstrate the algorithm’s compelling performance in industrial scenarios,outperforming the single-template matching algorithm by 21.3%,the multi-scale and multitemplate matching algorithm by 3.4%,the Faster RCNN algorithm by 19.7%,and the YOLOv9 algorithm by 17.3%in terms of solder joint detection accuracy.This optimized algorithm exhibits remarkable robustness and portability,ideally suited for detecting solder joints across diverse vehicle workpieces.Notably,this study’s dataset and feature fusion approach can be a valuable resource for other algorithms seeking to enhance their solder joint detection capabilities.This work thus not only presents a novel and effective solution for industrial solder joint detection but lays the groundwork for future advancements in this critical area.

Joint Detection for Image Transmission Based on Schur Algorithm over Wireless Sensor Network

To achieve much efficient multimedia transmission over an error-prone wireless network, there are still some problem must to be solved, especially in energy limited wireless sensor network. In this paper, we propose a joint detection based on Schur Algorithm for image wireless transmission over wireless sensor network. To eliminate error transmissions and save transmission energy, we combine Schur algorithm with joint dynamic detection for wireless transmission of JPEG 2000 encoded image which we proposed in [1]. Schur algorithm is used to computing the decomposition of system matrix to decrease the computational complexity. We de-scribe our transmission protocol, and report on its performance evaluation using a simulation testbed we have designed for this purpose. Our results clearly indicate that our method could approach efficient images transmission in wireless sensor network and the transmission errors are significantly reduced when compared to regular transmissions.

Oil Spill Management in Nigeria: SWOT Analysis of the Joint Investigation Visit (JIV) Process

The Joint Investigation Visit (JIV) process of the National Oil Spill Detection and Response Agency (NOSDRA) have been analysed using the Strength, Weakness, Opportunity and Threat (SWOT) methodology. The oil spill Joint Investigation Visit (JIV) is empowered by the Oil Spill Recovery, Clean-up, Remediation and Damage Assessment Reulations, 2011 Section 5. The strength of the JIV process lies within its participatory nature and the well defined legal structure of the process. The oil spill Joint Investigation Visit process in Nigeria has several weaknesses—lack of independence and oversight, lack of technical competence on the part of regulatory bodies, lack of technical competence on the part of community representative, lack of transparency on the part of oil companies, lack of general procedure for determining the actual cause of spill, lack of general procedure for determining the actual volume of oil spilled, determination on the size of the impacted area and exclusion of women from the JIV Process. The JIV process for oil spill presents a number of opportunities such as;increasing community awareness, growing consciousness through Non Governmental Organisations (NGOs) and capacity building of stakeholders. Possible threats to the JIV process include;poor governance and corruption, manipulation of the Process by the spiller through the start date of an oilspill and obvious lack of transparency. Improved effectiveness of the JIV process will depend on strengthening of government agency coordination, integrated decision-making adequate training to various stakeholders and supporting infrastructure for purposeful monitoring and enforcement.

LMMSE-based SAGE channel estimation and data detection joint algorithm for MIMO-OFDM system

A new channel estimation and data detection joint algorithm is proposed for multi-input multi-output （MIMO） - orthogonal frequency division multiplexing （OFDM） system using linear minimum mean square error （LMMSE）- based space-alternating generalized expectation-maximization （SAGE） algorithm. In the proposed algorithm, every sub-frame of the MIMO-OFDM system is divided into some OFDM sub-blocks and the LMMSE-based SAGE algorithm in each sub-block is used. At the head of each sub-flame, we insert training symbols which are used in the initial estimation at the beginning. Channel estimation of the previous sub-block is applied to the initial estimation in the current sub-block by the maximum-likelihood （ML） detection to update channel estimatjon and data detection by iteration until converge. Then all the sub-blocks can be finished in turn. Simulation results show that the proposed algorithm can improve the bit error rate （BER） performance.

Analysis of the joint detection capability of the SMILE satellite and EISCAT-3D radar

The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)satellite is a small magnetosphere–ionosphere link explorer developed cooperatively between China and Europe.It pioneers the use of X-ray imaging technology to perform large-scale imaging of the Earth’s magnetosheath and polar cusp regions.It uses a high-precision ultraviolet imager to image the overall configuration of the aurora and monitor changes in the source of solar wind in real time,using in situ detection instruments to improve human understanding of the relationship between solar activity and changes in the Earth’s magnetic field.The SMILE satellite is scheduled to launch in 2025.The European Incoherent Scatter Sciences Association(EISCAT)-3D radar is a new generation of European incoherent scatter radar constructed by EISCAT and is the most advanced ground-based ionospheric experimental device in the high-latitude polar region.It has multibeam and multidirectional quasi-real-time three-dimensional(3D)imaging capabilities,continuous monitoring and operation capabilities,and multiple-baseline interferometry capabilities.Joint detection by the SMILE satellite and the EISCAT-3D radar is of great significance for revealing the coupling process of the solar wind–magnetosphere–ionosphere.Therefore,we performed an analysis of the joint detection capability of the SMILE satellite and EISCAT-3D,analyzed the period during which the two can perform joint detection,and defined the key scientific problems that can be solved by joint detection.In addition,we developed Web-based software to search for and visualize the joint detection period of the SMILE satellite and EISCAT-3D radar,which lays the foundation for subsequent joint detection experiments and scientific research.

Novel Woods-Saxon stochastic resonance system for weak signal detection

We propose a joint exponential function and Woods–Saxon stochastic resonance(EWSSR)model.Because change of a single parameter in the classical stochastic resonance model may cause a great change in the shape of the potential function,it is difficult to obtain the optimal output signal-to-noise ratio by adjusting one parameter.In the novel system,the influence of different parameters on the shape of the potential function has its own emphasis,making it easier for us to adjust the shape of the potential function.The system can obtain different widths of the potential well or barrier height by adjusting one of these parameters,so that the system can match different types of input signals adaptively.By adjusting the system parameters,the potential function model can be transformed between the bistable model and the monostable model.The potential function of EWSSR has richer shapes and geometric characteristics.The effects of parameters,such as the height of the barrier and the width of the potential well,on SNR are studied,and a set of relatively optimal parameters are determined.Moreover,the EWSSR model is compared with other classical stochastic resonance models.Numerical experiments show that the proposed EWSSR model has higher SNR and better noise immunity than other classical stochastic resonance models.Simultaneously,the EWSSR model is applied to the detection of actual bearing fault signals,and the detection effect is also superior to other models.

一种TD-SCDMA下行链路信道估计的快速算法

以信道估计为基础的联合检测是TD＿SCDMA系统采用的关键技术之一,它能有效地消除多址干扰(MAI)和符号间干扰(ISI)从而提高系统容量.本文在分析TD＿SCDMA系统下行链路基带离散时间传输模型的基础上,提出了一种下行链路信道估计的快速算法,并结合终端联合检测算法进行了计算机仿真.结果表明该算法在提供同样信道估计性能的同时,计算量降低了近3阶.

Ping-pong effects study in PIC turbo joint detection for TDD CDMA

A kind of turbo joint detection scheme based on parallel interference cancellation （PIC） is studied; then, the eigenvalues of iteration matrix is deeply analyzed for studying the ping-pong effects in PIC JD and the corresponding compensation approach is introduced. Finally, the proposed algorithm is validated through computer simulation in TDD CDMA uplink transmission. The result shows that the ping-pong effects are almost avoided completely in the presence of the compensation scheme, and system performance is greatly improved.

Enhanced Deep Autoencoder Based Feature Representation Learning for Intelligent Intrusion Detection System

In the era of Big data,learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system(IDS).Owing to the lack of accurately labeled network traffic data,many unsupervised feature representation learning models have been proposed with state-of-theart performance.Yet,these models fail to consider the classification error while learning the feature representation.Intuitively,the learnt feature representation may degrade the performance of the classification task.For the first time in the field of intrusion detection,this paper proposes an unsupervised IDS model leveraging the benefits of deep autoencoder(DAE)for learning the robust feature representation and one-class support vector machine(OCSVM)for finding the more compact decision hyperplane for intrusion detection.Specially,the proposed model defines a new unified objective function to minimize the reconstruction and classification error simultaneously.This unique contribution not only enables the model to support joint learning for feature representation and classifier training but also guides to learn the robust feature representation which can improve the discrimination ability of the classifier for intrusion detection.Three set of evaluation experiments are conducted to demonstrate the potential of the proposed model.First,the ablation evaluation on benchmark dataset,NSL-KDD validates the design decision of the proposed model.Next,the performance evaluation on recent intrusion dataset,UNSW-NB15 signifies the stable performance of the proposed model.Finally,the comparative evaluation verifies the efficacy of the proposed model against recently published state-of-the-art methods.

Virtual Synchronous Generator Based Current Synchronous Detection Scheme for a Virtual Inertia Emulation in SmartGrids

Renewable energy sources, such as photovoltaic wind turbines, and wave power converters, use power converters to connect to the grid which causes a loss in rotational inertia. The attempt to meet the increasing energy demand means that the interest for the integration of renewable energy sources in the existing power system is growing, but such integration poses challenges to the operating stability. Power converters play a major role in the evolution of power system towards SmartGrids, by regulating as virtual synchronous generators. The concept of virtual synchronous generators requires an energy storage system with power converters to emulate virtual inertia similar to the dynamics of traditional synchronous generators. In this paper, a dynamic droop control for the estimation of fundamental reference sources is implemented in the control loop of the converter, including active and reactive power components acting as a mechanical input to the virtual synchronous generator and the virtual excitation controller. An inertia coefficient and a droop coefficient are implemented in the control loop. The proposed controller uses a current synchronous detection scheme to emulate a virtual inertia from the virtual synchronous generators. In this study, a wave energy converter as the power source is used and a power management of virtual synchronous generators to control the frequency deviation and the terminal voltage is implemented. The dynamic control scheme based on a current synchronous detection scheme is presented in detail with a power management control. Finally, we carried out numerical simulations and verified the scheme through the experimental results in a microgrid structure.

Bunch-length measurement at a bunch-by-bunch rate based on time–frequency-domain joint analysis techniques and its application

This paper presents a new technique for measuring the bunch length of a high-energy electron beam at a bunch-by-bunch rate in storage rings.This technique uses the time–frequency-domain joint analysis of the bunch signal to obtain bunch-by-bunch and turn-by-turn longitudinal parameters,such as bunch length and synchronous phase.The bunch signal is obtained using a button electrode with a bandwidth of several gigahertz.The data acquisition device was a high-speed digital oscilloscope with a sampling rate of more than 10 GS/s,and the single-shot sampling data buffer covered thousands of turns.The bunch-length and synchronous phase information were extracted via offline calculations using Python scripts.The calibration coefficient of the system was determined using a commercial streak camera.Moreover,this technique was tested on two different storage rings and successfully captured various longitudinal transient processes during the harmonic cavity debugging process at the Shanghai Synchrotron Radiation Facility(SSRF),and longitudinal instabilities were observed during the single-bunch accumulation process at Hefei Light Source(HLS).For Gaussian-distribution bunches,the uncertainty of the bunch phase obtained using this technique was better than 0.2 ps,and the bunch-length uncertainty was better than 1 ps.The dynamic range exceeded 10 ms.This technology is a powerful and versatile beam diagnostic tool that can be conveniently deployed in high-energy electron storage rings.

Target Detection on Water Surfaces Using Fusion of Camera and LiDAR Based Information

To address the challenges of missed detections in water surface target detection using solely visual algorithms in unmanned surface vehicle(USV)perception,this paper proposes a method based on the fusion of visual and LiDAR point-cloud projection for water surface target detection.Firstly,the visual recognition component employs an improved YOLOv7 algorithmbased on a self-built dataset for the detection of water surface targets.This algorithm modifies the original YOLOv7 architecture to a Slim-Neck structure,addressing the problemof excessive redundant information during feature extraction in the original YOLOv7 network model.Simultaneously,this modification simplifies the computational burden of the detector,reduces inference time,and maintains accuracy.Secondly,to tackle the issue of sample imbalance in the self-built dataset,slide loss function is introduced.Finally,this paper replaces the original Complete Intersection over Union(CIoU)loss function with the Minimum Point Distance Intersection over Union(MPDIoU)loss function in the YOLOv7 algorithm,which accelerates model learning and enhances robustness.To mitigate the problem of missed recognitions caused by complex water surface conditions in purely visual algorithms,this paper further adopts the fusion of LiDAR and camera data,projecting the threedimensional point-cloud data from LiDAR onto a two-dimensional pixel plane.This significantly reduces the rate of missed detections for water surface targets.

Digital image processing-based automatic detection algorithm of cross joint trace and its application in mining roadway excavation practice

This paper proposes a digital image processing-based detection algorithm for cross joint traces of coal roadway heading face.Initially,the acquired images were preprocessed,i.e.,adaptive correction was conducted for non-uniform illumination images based on the 2D gamma function.The edge detection algorithm was then applied to extract the edges of the structural plane,followed by the filtration of the non-structural plane noises.Moreover,the Hough transform algorithm was applied to extract the linear edges;finally,the edges were locally connected in accordance with the angle and distance criteria.The experimental results show that this algorithm can be used to reduce the noise caused by non-uniform illumination and avoid the mutual interference of multi-scale edges,so as to effectively extract the traces of the cross joint.Furthermore,Q-system and rock mass rating(RMR),were applied to conduct a quantitative evaluation on the stand-up time of unsupported roof in the four test images.The Q-system quality scores are 26.7,43.3,3.1,and 6.7,and the RMR quality scores are 56.84,58.73,48.42,and 51.42,respectively.The stand-up time of unsupported roofs with a span of 4.6 m are 30,36,7.7 and 14 d,respectively.

Novel Joint Chip Sampling and Phase Synchronization Algorithm for Multistandard UMTS Systems

CDMA Timing and phase offsets tracking remain as one of considerable factors that influence the performances of communication systems. Many algorithms are proposed to solve this problem. In general, these solutions process separately the chip sampling offset and phase rotation. In addition, most of proposed solutions can not assure a compromise between robustness criteria and low complexity for implementation in real time applications. In this paper we present an efficient algorithm for chip sampling and phase synchronization. This algorithm allows estimating and correcting jointly in real time, sampling instant and phase errors. The robustness and the low complexity of this algorithm are evaluated, firstly by simulation and then tested by real experimentation for UMTS standard. Simulation results show that the proposed algorithm provides very efficient compensation for sampling clock offset and phase rotation. A real time implementation is achieved, based on TigerSharc DSP, while using a complete UMTS transmission-reception chain. Experimental results show robustness in real conditions.

Diagnostic efficacy of combined detection of different tumor markers for primary hepatic carcinoma

Objective:To explore the clinical diagnostic value of combined detection of different tumor markers for primary hepatic carcinoma, and to provide the reference for the clinical diagnosis. Methods: 72 patients who were diagnosed with primary hepatic carcinoma were collected as observation group, 65 patients with benign liver disease as benign liver disease group and 80 cases of health examination as healthy control group, the contents of tumor markers alpha fetoprotein(AFP), carcinoembryonic antigen(CEA), carbohydrate antigen-199(CA199), carbohydrate antigen-125(CA125) and carbohydrate antigen-153(CA153) were determined by electrochemiluminescence in all subjects, then the results of five kinds of tumor markers and the positive rates of each index between the two groups were compared, the diagnostic value of separate and combined detection of different tumor markers in primary hepatic carcinoma were analyzed.Results: The values of AFP, CA199 and CA153 in the observation group were higher than the benign liver disease group, the values of AFP, CEA, CA199, CA125 and CA153 in the observation group were higher than the control group, the values of CA199 and CA125 in the benign liver disease group were higher than the control group, the differences were statistically significant (P<0.05). The positive rates of AFP, CEA, CA199 and CA153 in the observation group were higher than the benign liver disease group, the positive rates of AFP, CEA, CA199 and CA125 in the observation group were higher than the control group, the positive rates of AFP, CEA, CA199 and CA125 in the benign liver disease group were higher than the control group, the differences were statistically significant (P<0.05). The sensitivity of combined detection of all indicators for primary hepatic carcinoma was 86.4%, specificity, correct index, misdiagnosis rate and missed diagnosis rate were 86.4%, 89.2%, 75.6%, 13.6% and 10.8% respectively, and the combined detection was higher than the correct index of each index.Conclusion: Combined detection of serum tumor markers AFP, CEA, CA199, CA125 and CA153 can improve the sensitivity and specificity of diagnosis of primary hepatic carcinoma, it has better diagnostic value for primary hepatic carcinoma.

Synchronous detection of multiple optical characteristics of atmospheric aerosol by coupled photoacoustic cavity

Owing to the influence of sampling loss,cavity difference and detecting source,the multi-optical parameter measurement of atmospheric aerosol cannot be detected simultaneously in the same reference environment.In order to solve this problem,a new method of simultaneously detecting the aerosol optical parameters by coupling cavity ring-down spectrometer with photoacoustic spectroscopy is proposed.Firstly,the coupled photoacoustic cavity is formed by the organic fusion of the photoacoustic cavity and the ring-down cavity.Secondly,the integrated design of the coupling spectroscopy system is carried out.Finally,the extinction coefficient and absorption coefficient of aerosol are measured simultaneously by the system,and then the aerosol scattering coefficient and single albedo are calculated indirectly.The accuracy of the system is verified by comparing with the data from the environmental quality monitoring station,which provides a new idea for the detection of multi-optical characteristics of atmospheric aerosol.

End-to-End Joint Multi-Object Detection and Tracking for Intelligent Transportation Systems

Environment perception is one of the most critical technology of intelligent transportation systems(ITS).Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking(MOT).However,most existing MOT algorithms follow the tracking-by-detection framework,which separates detection and tracking into two independent segments and limit the global efciency.Recently,a few algorithms have combined feature extraction into one network;however,the tracking portion continues to rely on data association,and requires com‑plex post-processing for life cycle management.Those methods do not combine detection and tracking efciently.This paper presents a novel network to realize joint multi-object detection and tracking in an end-to-end manner for ITS,named as global correlation network(GCNet).Unlike most object detection methods,GCNet introduces a global correlation layer for regression of absolute size and coordinates of bounding boxes,instead of ofsetting predictions.The pipeline of detection and tracking in GCNet is conceptually simple,and does not require compli‑cated tracking strategies such as non-maximum suppression and data association.GCNet was evaluated on a multivehicle tracking dataset,UA-DETRAC,demonstrating promising performance compared to state-of-the-art detectors and trackers.