Joint Identification Research Articles

Multistage deep learning methods for automating radiographic sharp score prediction in rheumatoid arthritis

The Sharp-van der Heijde score (SvH) is crucial for assessing joint damage in rheumatoid arthritis (RA) through radiographic images. However, manual scoring is time-consuming and subject to variability. This study proposes a multistage deep learning model to predict the Overall Sharp Score (OSS) from hand X-ray images. The framework involves four stages: image preprocessing, hand segmentation with UNet, joint identification via YOLOv7, and OSS prediction utilizing a custom Vision Transformer (ViT). Evaluation metrics included Intersection over Union (IoU), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Huber loss, and Intraclass Correlation Coefficient (ICC). The model was trained using stratified group 3-fold cross-validation on a dataset of 679 patients and tested externally on 291 subjects. The joint identification model achieved 99% accuracy. The ViT model achieved the best OSS prediction for patients with Sharp scores < 50. It achieved a Huber loss of 4.9, an RMSE of 9.73, and an MAE of 5.35, demonstrating a strong correlation with expert scores (ICC = 0.702, P < 0.001). This study is the first to apply a ViT for OSS prediction in RA. It presents an efficient and automated alternative for overall damage assessment. This approach may reduce reliance on manual scoring.

Multi-DoFs nonlinear joint identification through substructure decoupling

Multi-DoFs nonlinear joint identification through substructure decoupling

Joint Identification and Sensing for Discrete Memoryless Channels.

In the identification (ID) scheme proposed by Ahlswede and Dueck, the receiver's goal is simply to verify whether a specific message of interest was sent. Unlike Shannon's transmission codes, which aim for message decoding, ID codes for a discrete memoryless channel (DMC) are far more efficient; their size grows doubly exponentially with the blocklength when randomized encoding is used. This indicates that when the receiver's objective does not require decoding, the ID paradigm is significantly more efficient than traditional Shannon transmission in terms of both energy consumption and hardware complexity. Further benefits of ID schemes can be realized by leveraging additional resources such as feedback. In this work, we address the problem of joint ID and channel state estimation over a DMC with independent and identically distributed (i.i.d.) state sequences. State estimation functions as the sensing mechanism of the model. Specifically, the sender transmits an ID message over the DMC while simultaneously estimating the channel state through strictly causal observations of the channel output. Importantly, the random channel state is unknown to both the sender and the receiver. For this system model, we present a complete characterization of the ID capacity-distortion function.

Handling Distinct Correlated Effects with CCE

AbstractThe common correlated effects (CCE) approach by Pesaran is a popular method for estimating panel data models with interactive effects. Due to its simplicity, i.e., unobserved common factors are approximated with cross‐section averages of the observables, the estimator is highly flexible and lends itself to a wide range of applications. Despite such flexibility, however, the properties of CCE estimators are typically only examined under the restrictive assumption that all the observed variables load on the same set of factors, which ensures joint identification of the factor space. In this article, we take a different perspective, and explore the empirically relevant case where the dependent and explanatory variables are driven by distinct but correlated factors. Hence, we consider the case of Distinct Correlated Effects. Such settings can be argued to be relevant for practice, for instance in studies linking economic growth to climatic variables. In so doing, we consider panel dimensions such that as , which is known to induce an asymptotic bias for the pooled CCE estimator even under the usual common factor assumption. We subsequently develop a robust bootstrap‐based toolbox that enables asymptotically valid inference in both homogeneous and heterogeneous panels, without requiring knowledge about whether factors are distinct or common.

Healthcare Students' Exposure to Sexual Violence During Clinical Placements in Spain: A Cross‐Sectional Study

ABSTRACTData from international investigations indicate that gender‐based violence is a phenomenon that also occurs in the university environment. Despite the fact that clinical learning environments have been widely studied, little attention has been paid to sexual violence in the clinical placement setting of Health Sciences students. Consequently, the aim of this study was to describe situations and/or behaviours of harassment, such as assault and/or sexual abuse experienced by the students of one university in Barcelona, Spain, during their clinical placements. A cross‐sectional study was conducted using an ad‐hoc online questionnaire. A convenience sample was used including bachelor's degree students as subjects during their clinical placements in the second half of 2022. A total of 156 responses were collected from the students of four degrees (Nursing, Physiotherapy, Nutrition and Dietetics and Pharmacy). Of the total results collected, 74.5% (117) corresponded to women and the remaining 25.5% (40) to men. 71.3% of students reported that they had experienced one (or more) of the 25 types of conduct described in the questionnaire at some time. The behaviours of social interaction with sexual content and sexual harassment in the placement setting showed that such behaviours were mostly experienced by women on the Nursing degree. In relation to the perpetrators of such behaviours, the respondents manifested that the greatest aggressor was a patient and that most of the aggression took place in hospitalisation units, rehabilitation centres and nursing homes. It is essential that academic institutions, in collaboration with health institutions, carry out joint actions, awareness‐raising, identification and interventions, with students, professionals and teams from a culture of non‐violence and the eradication of sexual harassment in academic placements.

Binary Observation‐Based FIR System Identification Against Replay Attacks

ABSTRACTIn the context of the increasing security issues of cyber‐physical systems (CPSs), this paper addresses the parameter identification of binary observation‐based finite impulse response (FIR) systems under replay attacks, overcoming the problem of high nonlinearity of quantized systems and greater data sparsity caused by replay attacks. For the attacker, based on the energy‐constrained condition, an optimization attack model is established to maximize the absolute error of identification, giving the method of obtaining the optimal attack strategy. Following the defender, the identifiability of unknown parameters is discussed and a robust defense scheme is proposed. This scheme involves a joint identification strategy for both the attack strategy and unknown parameters. By enhancing the excitability of system inputs, consistent identification is ensured despite replay attacks. An algorithm for the accomplishment of the joint identification strategy is presented based on the grid search method. Rationality of the method is confirmed with performing numerical simulations.

A body welded joint quality prediction method incorporating reconstruction error

With the continuous development of artificial intelligence technology, the application of machine learning in the prediction of the quality status of body welded joints is becoming more and more widespread. However, due to the strict setting of production parameters, the proportion of abnormal weld joints is relatively small, resulting in unbalanced data distribution. This poses a challenge to accurately assess welded joint quality and effectively identify abnormal welded joints. To address this problem, a quality prediction method incorporating reconstruction errors is proposed, aiming to accurately predict the quality of welded joints and effectively identify abnormal welded joints. In this paper, noise-reducing autoencoder (DAE) is used to reconstruct the feature data. It not only highlights the key features, but also extracts the reconstruction error as a new feature used to help the model identify abnormal weld joints. Because the reconstruction error of abnormal welded joints is bigger. Then, the reconstruction error and reconstruction features are used as inputs, and the welded joint quality score is the output. The support vector regression (SVR) model incorporating particle swarm algorithm (PSO) is used to construct the mapping relationship between input and output to achieve the prediction of welded joint quality status. The experimental results show that the method is highly reliable in the prediction of welded joint quality status, and the prediction accuracy is significantly better than other models. Compared with other methods, the accuracy of abnormal welded joint identification is not more than 82%, and the accuracy of abnormal welded joint identification of this method is more than 92%. This study provides technical support for the intelligent online inspection of welded joints in automobile bodies.

Serological and Molecular Biological Analysis of Rare Cases of p-Blood Type

To clarify the specificity of patient blood type and blood type antibody through patient blood type serological tests and molecular biology results, and to search for matching blood for patients. Blood type serological methods were used for the identification of red blood cell ABO, RhD, and p blood type. Salt water method, microcolumn gel method and IAT method were used for irregular antibody screening and antibody specificity identification. Forward and reverse sanger sequencing was used to amplify specifically the third exon (CDS sequence) of the A4GALT gene, identify the mutation site and genotype of the gene. The patient's serological blood type was positive for type B and D, indicating a p phenotype. At the same time, anti-Tja was detected in the serum, and further sequencing of the A4GALT gene exon was performed, the homozygous mutation with G>C occurred at base 559, and the genotype ISBT was named A4GALT*01N.10. Molecular biology verification confirmed it to be p blood group. Serological methods confirm that the patient's serum contains anti-Tja. The joint identification of the rare blood type-p blood type by serological and molecular biological methods is of great significance for safe clinical blood transfusion.

Joint Screening and Identification of Potential Targets of Nitazoxanide by Affinity Chromatography and Label-Free Techniques.

Nitazoxanide not only exhibits a broad spectrum of activities against various pathogens infecting animals and humans but also induces cellular autophagy. Currently, the pattern of action and subcellular targets of nitazoxanide-induced cellular autophagy are still unclear. To identify potential targets of nitazoxanide in mammalian cells, we developed an af-finity chromatography system using tizoxanide, a deacetyl derivative of nitazoxanide, as a ligand. Affinity chromatography was performed using VERO cell extracts on tizoxanide-biotin, and the isolated binding proteins were identified by mass spectrometry. Candidate target proteins ob-tained using affinity chromatography were co-analysed with the drug affinity response target sta-bility method. Fluorescent probes obtained by coupling rhodamine B to nitazoxanide were used for intracellular localisation of the binding targets. Solvent-induced protein precipitation profiling and thermal proteome profiling were used to further validate the binding proteins. The joint analysis of the drug affinity response target stability method and affinity chro-matography resulted in the screening of six possible candidate target proteins. Fluorescent probes localised the nitazoxanide-binding protein around the nuclear membrane. Molecular docking re-vealed that the binding proteins mainly formed hydrogen bonds with the nitro group of nitazoxa-nide. Solvent-induced protein precipitation profiling and thermal proteome profiling further vali-dated SEC61A, PSMD12, and PRKAG1 as potential target proteins of nitazoxanide. The data supports the idea that nitazoxanide is a multifunctional compound with multiple targets.

Tensor-based statistical learning methods for diagnosing product quality defects in multistage manufacturing processes

Multistage manufacturing processes with identical stages provide three-dimensional process data in which the first dimension represents the process (control/sensing) variable, the second is the stage, and the third is the measurement/sampling/data acquisition time point. Diagnosing quality faults in such processes often requires the simultaneous identification of crucial process variables and stages associated with product quality anomalies. Most existing diagnosis methods convert 3D data into a 2D matrix, resulting in loss of information and reduced diagnostic accuracy and stability. To address this challenge, we propose a penalized tensor regression model that regresses the product quality index against its 3D process data. For the estimation of high-dimensional regression coefficients with the limited amount of historical data, we apply the CANDECOMP/PARAFAC and Tucker decompositions to the coefficient tensor, which significantly reduces the number of parameters to be estimated. Based on the decompositions, a new regularization term is designed to enable the joint identification of critical process variables and stages. To estimate the parameters, we develop the block coordinate proximal descent algorithm and provide its convergence guarantee. Numerical studies demonstrate that the proposed methods can enhance diagnostic stability and on average improve the diagnostic accuracy by around 20% over existing benchmarks.

Optimized Radio Frequency Footprint Identification Based on UAV Telemetry Radios.

With the widespread use of unmanned aerial vehicles (UAVs), the detection and identification of UAVs is a vital security issue for the safety of airspace and ground facilities in the no-fly zone. Telemetry radios are important wireless communication devices for UAVs, especially in UAVs beyond the visual line of sight (BVLOS) operating mode. This work focuses on the UAV identification approach using transient signals from UAV telemetry radios instead of the signals from UAV controllers that the former research work depended on. In our novel UAV Radio Frequency (RF) identification system framework based on telemetry radio signals, the EC-α algorithm is optimized to detect the starting point of the UAV transient signal and the detection accuracy at different signal-to-noise ratios (SNR) is evaluated. In the training stage, the Convolutional Neural Network (CNN) model is trained to extract features from raw I/Q data of the transient signals with different waveforms. Its architecture and hyperparameters are analyzed and optimized. In the identification stage, the extracted transient signals are clustered through the Self-Organizing Map (SOM) algorithm and the Clustering Signals Joint Identification (CSJI) algorithm is proposed to improve the accuracy of RF fingerprint identification. To evaluate the performance of our proposed approach, we design a testbed, including two UAVs as the flight platform, a Universal Software Radio Peripheral (USRP) as the receiver, and 20 telemetry radios with the same model as targets for identification. Indoor test results show that the optimized identification approach achieves an average accuracy of 92.3% at 30 dB. In comparison, the identification accuracy of SVM and KNN is 69.7% and 74.5%, respectively, at the same SNR condition. Extensive experiments are conducted outdoors to demonstrate the feasibility of this approach.

Applications and challenges of DNA barcoding in rapid radiation groups: Rhodiola (Crassulaceae) as a case study

ObjectiveRhodiolae Crenulatae Radix et Rhizoma (Hongjingtian in Chinese, RCRR), the roots and rhizomes of Rhodiola crenulata and its application in the medicinal market is very chaotic. In this study, DNA barcoding database and identification engine of Rhodiola species were established, decoction pieces from the medicinal market were identified, and the application and challenges of DNA barcoding in the rapid radiation of Rhodiola species were analyzed. This study provides reference for the protection, rational development, and utilization of endangered resources within Rhodiola species. MethodsA total of 50 original plant samples from 20 species of the genus Rhodiola from Hebei, Xinjiang, Tibet, Jilin, and other major production areas were collected. Theses samples cover the typical distribution area (Qinghai-Tibetan Platea) of Rhodiola species and other scattered alpine regions (Changbai Mountain, Taibai Mountain, Lushan Mountain, etc.), it encompasses all Rhodiola species with thick rhizomes in China. ITS2 and psbA-trnH barcode of Rhodiola database (BORD) were established and an identification engine named Rhodiola-IDE was developed. The stability and accuracy of the standard DNA barcoding database were evaluated using two datasets. Rhodiola-IDE identified 31 decoction pieces of RCRR from the medicinal material market. ResultsThe BORD containing 1 532 sequences of 88 Rhodiola species has been established, and the identification efficiency results showed good accuracy and stability. According to the Chinese Pharmacopoeia (2020 edition), 23 samples (74.2%) were identified as authentic R. crenulata, while the rest of the marketed varieties were R. kirilowii, R. dumulosa, and R. fastigiata. The product label “Larger flower, Hongjingtian” was identified as R. crenulata. Samples labeled as “Smaller flower, Hongjingtian” were identified as R. crenulata, R. kirilowii, and R. fastigiata. ConclusionITS2 and psbA-trnH barcodes can identify monophyletic groups represented by R. crenulata. However, for non-monophyletic species, it is necessary to collect as many samples as possible and combine them with multiple markers for joint identification. This study discussed the application and challenges of DNA barcodes in Rhodiola under rapid radiation conditions, providing a scientific basis for the rational development and utilization of Rhodiola varieties.

Joint identification of contaminant source and dispersion coefficients based on multi-observed reconstruction and ensemble Kalman filtering

Joint identification of contaminant source and dispersion coefficients based on multi-observed reconstruction and ensemble Kalman filtering

Welded joints stiffness and damping characterisation based on model updating and texture analysis

Welded structures are integral to complex bodies such as ships and offshore rigs. Welded joints are frequently used in connecting various plates and substructures, making their assessment pivotal. The numerical model used here consists of plates coupled using springs and dashpots. The coupling models the welded joint and exemplifies the uncertainties in the welding process. The joint identification algorithm systematically defines the coupling in two steps: first, by updating the model for spring stiffness and later, by identifying the dashpot coefficients. The dynamic characteristic is determined using a frequency response function (FRF). After model updating and damping identification, a comparison between the updated FRF and that obtained from the experimental modal test is carried out to ascertain the efficiency of the algorithm. Further, the test structure is subjected to experimental testing to correlate the results from the model updating with material properties. The experimental study includes tensile testing of the weld joints and microstructural analysis. The characterisation is carried out by partitioning the welded joint. The joint stiffness and damping are correlated to the stress–strain response and crystallographic texture of the welded plate.

Joint identification of groundwater pollution source information, model parameters, and boundary conditions based on a novel ES-MDA with a wheel battle strategy

Groundwater pollution source identification (GPSI) is an important prerequisite for pollution remediation and risk assessment. However, an accurate GPSI is usually difficult to achieve due to the need to simultaneously consider practical problems and theoretical research. For practical problems, the boundary conditions, especially the concentration boundary, are often given as known constants through prior information. However, in most practical situations, the boundary conditions are complex and cannot be accurately estimated in advance, which leads to the distortion of the final identification results. Therefore, this study focused on the concentration boundary, and first proposed to jointly identify three types of unknown variables (source information, model parameters, and boundary conditions) to ensure that the identification results had more practical value. For theoretical research, as the number of unknown variable types increases, the difficulty of solving the inverse problem often increases, which may lead to inaccurate inversion results. Thus, a novel ensemble smoother with multiple data assimilation (ES-MDA) with a wheel battle strategy was proposed, enhancing the identification accuracy. We designed two cases to verify the effectiveness and practicality of the above ideas: a low-dimensional Case 1 (including four different synthetic scenarios: three scenarios with unknown boundary conditions under three concentration boundary modes and a scenario with known boundary conditions) and a high-dimensional complex Case 2. Identifying the boundary conditions in GPSI was found to be of great significance. Compared to the standard ES-MDA method, the ES-MDA with a wheel battle strategy proposed here could improve the inversion accuracy, and had certain effectiveness and practicality.

Joint identification method for thermal–mechanical coupling loads

The crucial role in evaluating structural fatigue life, implementing health monitoring, and optimizing lightweight and reliable design is played by load identification technology. With the development of the aviation industry towards high-speed aircraft, the challenges under high temperature and high load conditions have been intensified. Unfortunately, in environments characterized by the coexistence of force and heat, difficulties are faced by existing load identification methods in achieving key decoupling and accurate identification of force and temperature fields. To address this gap, the Joint Identification Method (JIM), a new method that integrates force and thermal strain patterns, is introduced in this article. This method has been developed through in-depth research on “joint physical fields” and “joint basis functions.” Multiple physical fields for load identification are systematically decomposed by our method, each field is theoretically deduced, and they are seamlessly reassembled using the same structural spatial field. Through the method, mechanical strain and thermal strain are separated in high-precision temperature field reconstruction, and a basis function method for identifying the full field load distribution under a few measurement points is obtained. To verify the accuracy of our proposed Joint Identification Method (JIM), five experiments were designed, and the results consistently demonstrated the superiority of our method in accuracy (with an error always below 5%), efficiency (avoiding a large number of redundant calculations in traditional Duhamel methods), and reliability in static and dynamic load static heat identification. In the end, accurate, efficient, and reliable identification of the structural strain field, structural load field, and temperature field were achieved by our method.

The Excavation of the Remains of Soldiers Killed in the Korean War: Implications and International Cooperation

The main purpose of this article is to explore the significance of the objectives by focusing on activity theory, as well as the legal and institutional establishment of the Ministry of National Defense (MND) Agency for KIA Recovery &amp; Identification (MAKRI), the subject of the Korean War remains excavation project, and the implications of expanding the excavation of remains in the DMZ and the repatriation of the remains of US and UN soldiers on the 70th anniversary of the Armistice Agreement. The excavation project, which began in 2000, aimed to build public trust by realizing the noble will of national unlimited responsibility that “the state is responsible for those sacrificed for the country forever.” Thus, it has allowed social integration beyond ideological and generational conflicts. It expanded the issue of excavating remains from South Korea to Arrowhead Hill and White Horse Hill in the DMZ area under the September 19 Inter‐Korean Military Agreement in 2018. Also, the ROK–US MOU was signed in 2000 in terms of international cooperation in the excavation of remains and carries out joint identification and repatriation of remains. The repatriation of the remains of Chinese troops and the remains of UN combatant countries has important implications for strengthening military diplomacy in legal and humanitarian aspects.

ERPCA: Robust Principal Component Analysis for Exponential Family Distributions

AbstractRobust principal component analysis (RPCA) is a widely used method for recovering low‐rank structure from data matrices corrupted by significant and sparse outliers. These corruptions may arise from occlusions, malicious tampering, or other causes for anomalies, and the joint identification of such corruptions with low‐rank background is critical for process monitoring and diagnosis. However, existing RPCA methods and their extensions largely do not account for the underlying probabilistic distribution for the data matrices, which in many applications are known and can be highly non‐Gaussian. We thus propose a new method called RPCA for exponential family distributions (), which can perform the desired decomposition into low‐rank and sparse matrices when such a distribution falls within the exponential family. We present a novel alternating direction method of multiplier optimization algorithm for efficient decomposition, under either its natural or canonical parametrization. The effectiveness of is then demonstrated in two applications: the first for steel sheet defect detection and the second for crime activity monitoring in the Atlanta metropolitan area.

Intertwined rural and urban policing in the local communities of the Pomurje region in Slovenia

PurposeThe purpose of the paper is to present the results of research in the region covered by the Murska Sobota Police Directorate based on semi-structured interviews we conducted with community policing officers in the rural and urban areas of Pomurje.Design/methodology/approachCommunity policing is one of the more recent (yet hardly new) ways of ensuring security. It focuses on collaboration between citizens and the police, the joint identification of security issues and resolving them. Not long ago, it was established that policing varies depending on geographical criteria, specifically the urbanisation of the environment since police work in cities is often very narrowly specialised compared to in a rural environment. We were therefore interested in whether the Pomurje region also shows that it is easier to practise community policing in rural areas compared to cities.FindingsThe research results confirm previous findings; namely, that in urban areas approximately two-thirds of the population does not know the community policing officer, leading to the mutual cooperation between residents and the police being poorer than in rural areas. The conclusion describes ways of improving the established situation facing Pomurje.Research limitations/implicationsWe see the limitations of the study in the peculiarities of the Pomurje region; therefore the results cannot be generalised and applied in areas of other police directorates.Originality/valueThe survey offers insight into rural and urban policing in the Pomurje region at the same time, focusing on possibilities for improvements.

Research and Optimization of Tubular Column Joint Identification Technology for Intelligent Drilling Platforms

Research and Optimization of Tubular Column Joint Identification Technology for Intelligent Drilling Platforms