Hyperparathyroidism (HPT) and thyroid nodule (TN) are caused by abnormalities in the parath/yroid and thyroid glands, respectively. Due to their proximity, small size, and similar ultrasound characteristics, the traditional object detection algorithms often struggle to accurately differentiate between HPT and TN when both lesions coexist in ultrasound images, leading to a high rate of misdiagnosis. In order to achieve accurate detection of HPT and TN when the two lesions coexist, we constructed three comprehensive object detection datasets: one containing only hyperparathyroidism (HPTD), one containing only thyroid nodules (TND), and one mixed dataset that includes both types of lesions (HPT-TND). A novel multi-branch feature fusion DETR network (MB-DETR) is proposed based on the Real-Time Detection Transformer (RT-DETR) model. We redesigned the feature fusion module and incorporated asymmetric convolution to enhance feature extraction. To validate the proposed MB-DETR performance, the experiments have been carried out on the three datasets. Our model achieved a superior performance compared to the state-of-the-art object detection models in the key metrics such as F1, Precision, and Recall, while significantly reducing computational costs. Additionally, the ablation studies confirmed the effectiveness of asymmetric convolution and the multi-branch feature fusion module in terms of enhancement of detection performance. The experimental results show that the Multi-Branch Feature Fusion incorporated with the asymmetric convolution improves the local feature extraction capability of the DETR model. It is concluded that the proposed MB-DETR model outperforms the existing ones in the detection of TN and HPT when both lesions coexist and thus effectively assists in the diagnosis of the correlated disease.

Precise automated detection of parathyroid nodules is critical for enhancing the diagnosis and treatment of hyperparathyroidism (HPT). However, the ectopic nature, varied shapes, fuzzy boundaries, and significant patient variability of these nodules challenge traditional object detection methods. To tackle these issues, we present ASW-YOLO, an advanced detection framework built on YOLOv10, designed to address the limitations of medical imaging object detection. First, we introduce the Feature Fusion Assembly Block (FFAB), which integrates convolutional and Transformer-based modules to better capture the global context of parathyroid nodules. Second, we propose the Squeeze-Enhanced Axial Feature Pyramid Network (SEA-FPN), which uses adaptive weighting of high-level features to refine low-level feature selection and incorporates axial squeeze attention to balance detection accuracy and processing speed. Additionally, we adopt the WIoU v3 loss function to enhance bounding box accuracy by dynamically adjusting gradient gains, reducing the impact of unclear boundaries. Experiments on a specialized parathyroid ultrasound dataset show that ASW-YOLO significantly outperforms the baseline model, achieving a 95.3% mAP@50, with a 3.2% increase in recall and a 10.2% reduction in parameters. These results highlight the potential of ASW-YOLO for real-time, high-precision computer-aided diagnosis in clinical practice.

Existing domain adaptation (DA) methods, which focus on realizing the class-level alignment for cross-domain features to solve the problem of fuzzy classification boundaries in feature learning process, however, it is difficult to deal with samples located at the classification boundary in this way, resulting in diagnosis performance being limited. To solve the problem, this article proposes a semi-supervised contrastive DA network (SSCDAN) for realizing the cross-working condition rotating machinery fault diagnosis method. Specifically, an in-domain semi-supervised contrastive learning (SSCL) strategy is designed, with the supervision of class information, which guides the discriminative learning of different classes in each domain to eliminate the fuzzy classification boundaries, and facilitates the DA of cross-domain features which utilizes the local maximum mean discrepancy (LMMD). Meanwhile, the negative impact from poor-quality target domain pseudo-labels on SSCL and DA is mitigated by dynamically limiting the associated contrastive learning loss and DA loss gain, and introducing domain adversarial. Finally, the effectiveness of SSCDAN is validated by the ablation and comparison experiments on the Paderborn University (PU) and wind turbine gearbox (WTG) datasets. Compared with deep subdomain adaptation (DSAN), SSCDAN improves the overall average accuracy by 22.43% and 7.36% on the cross-working condition diagnosis tasks of PU and WTG datasets, respectively, and outperforms other popular DA methods.

Within the context of the Industrial Internet of Things (IIoT), interorganizational collaboration and data interchange enable enhanced data analytics and extraction. The joint data mining yields improved data access, analytical precision, and uncovers concealed patterns. However, during the process of joint data mining, there are data privacy concerns such as data leakage and misuse, necessitating the protection of data privacy. In this article, we propose a secure multiparty computation (MPC) approach, aiming to achieve the separation of data ownership and usage which ensures that multiple parties can perform computations without revealing sensitive inputs. Additionally, we utilize Beaver triples to reduce polynomial degrees and lower communication costs. Furthermore, we demonstrate consistency checks and computation verification to attest to the result consistency and data confidentiality under the semihonest adversary model. When evaluated with 20 parties on an Intel i7-10700 CPU, a single 128-bit field multiplication transmits 2.6 KB and completes in 21 ms over a 1 Mbps link. Even at 500 parties the budget remains 62.6 kB (490 ms). Compared with six representative MPC and homomorphic schemes, the proposed design achieves the lowest overall communication cost and competitive computation time. Integrated into a privacy-preserving DBSCAN implementation, the framework clusters 1000 2-D points with plaintext-level accuracy in millisecond-level, demonstrating practical applicability to multisource data mining.

The remaining useful life (RUL) prediction of rolling element bearings is usually subject to the following limitations. First, it is difficult to obtain the massive performance degradation data, which resulting in the insufficient learning of the historical degradation law. Second, the parameters in most of existing models depend heavily on the manual selection, which leads to the poor generalization performance. To address these problems, a novel adaptive sparse graph learning (ASGL) method based on digital twin dictionary (DTD) is proposed in this article. To facilitate the prediction when the data are insufficient, the extended exponential models and the extended linear piecewise models are first established, then a DTD that covers the various degradation behaviors is constructed. Besides, a new objective function of graph learning is designed and the sparse regularization method is introduced to adaptively obtain the topology graph of data. Therefore, the method avoids the wrong adjacency relationship caused by inappropriate parameters. The simulation and experimental results show that the DTD has higher prediction accuracy than the experimental samples, and the ASGL method is easy to implement and has lower dependence on the parameter selections. In addition, compared with some state-of-the-art methods, it can obtain better RUL prediction results.

Hyperparathyroidism (HPT) is an endocrine disorder characterized by the excessive synthesis and secretion of parathyroid hormone. The excessive parathyroid hormone can cause bone calcium loss, vascular calcification, and cardiovascular diseases. Because of the multiple and reversible distribution of HPT, the accurate localization of the lesion during clinical diagnoses is challenging, often resulting in misdiagnoses or missed diagnoses. The computer-aided diagnosis (CAD) can improve the diagnostic accuracy. The joint utilization of the Squeeze-and-Excitation (SE) attention module and deformable transformer networks can achieve excellent detection performance for HPT. By improving the weight mapping approach within the SE module, we effectively enhanced the joint network’s detection performance for HPT.

Hyperparathyroidism (HPT) is a symptom of hypertrophy and enlargement of the parathyroids due to the overproduction of parathyroid hormones. Since there are multiple ectopic parathyroid glands, namely the position of the glands is not fixed. The clinical diagnosis and localization of HPT are difficult, which can easily results in underdiagnoses and misdiagnoses. This paper collected and produced two versions of datasets for the computer-aided diagnosis (CAD) of HPT in PASCAL VOC and COCO formats, respectively. Statistical analysis shows that 84.5% of the HPT lesions in the ultrasonic images are medium-scale and large-scale targets. We proposed a hybrid detection network that employs CSPDarknet, spatial pyramid pooling net (SPPnet) and deformable transformer for automatically detecting HPT. We further introduced several attention networks combined with the CNN backbone to improve the network structure. Extensive experiments have been carried out on the datasets by combining various attention networks and the hybrid detection network. The experimental results illustrate that the inclusion of the channel attention mechanisms can improve the performance of HPT lesion detection more effectively than other hybrid networks, especially for large lesion objects. It implies that the channel-wise features are more critical for HPT detection than the global features and the spatial location features.

To simultaneously monitor some electrical or mechanical faults of an in-wheel motor and intelligently evaluate the operation safety, in this article, a fuzzy system of operation safety assessment (OSA) is proposed in this article. This method first uses many symptom parameters (SPs), such as root mean square, crest factor, temperature rise, and current covariance to express the features of the electrical and mechanical faults from different perspectives, such as vibration, noise, temperature, current, and voltage; possibility theory is employed to translate the probability density function of each SP into the possibility function, and sample data are gradually updated to optimize the possibility function for obtaining the SPs’ membership functions that are evaluation models. Second, the probabilities of the current operation state, that is safety, attention, or danger, are obtained from each evaluation model in a stage. Picture fuzzy set (PFS) is used to define a basic picture fuzzy number (PFN); thus, many PFNs from multiple models and multiple stages are used to establish an OSA's decision matrix. Third, Mahalanobis distance is reintegrated into PFS's theory for objectively judging the real-time evaluation information, and the best–worst method is used to estimate subjectively the initial evaluation experience; thus, the multimodel linkage mechanism is designed. Finally, TODIM is modified to define the relative safety ratio, and prospect theory is employed to structure the global index for formulating the multistage collaboration approach; thus, a fuzzy OSA's system is established. The effectiveness of the proposed method was verified by the experimental analysis for the operation safety of in-wheel motor with electrical and mechanical faults.

Hyperparathyroidism (HPT) is a disorder in which the parathyroid glands produce too much parathyroid hormone (PTH), which may lead to hypocalcemic convulsions, cardiomyopathy, hypertension and other diseases, even threaten the lives of patients under certain severe conditions. Since HPT is usually multiple and ectopic with variable symptoms, the diagnosis and location of HPT is a difficult task even for senior radiologists. A transfer learning-based computer-aided diagnosis (CAD) approach is proposed for automated recognition of HPT in this paper. A dataset of the brightness-mode ultrasound images is developed for the HPT recognition, which is usually annotated by senior radiologists. We addressed the HPT recognition using the various computer vision algorithms on the HPT dataset and obtained good performances for all the algorithms. The experimental results demonstrated that the dataset is effective in aiding the diagnosis of HPT.

A step-by-step fuzzy diagnostic method based on frequency-domain symptom extraction and trivalent logic fuzzy diagnosis theory (TLFD), which is established by combining the trivalent logic inference theory with the possibility and fuzzy theories, is proposed herein. The features for diagnosing a number of abnormal states are extracted sequentially from the measured signals using statistical tests in the frequency domain. The symptom parameters (SPs) that can sensitively reflect symptoms of abnormal states are then selected to provide effective information for the discrimination of each state. The membership function of each state is then generated based on the possibility theory using the probability functions of the SPs. The step-by-step fuzzy diagnoses are performed based on the TLFD. This method can be used extensively to diagnose anomalies in various equipment. In this study, the diagnosis of structure faults of a rotating machine is cited as an example to demonstrate the effectiveness and universality of this method.

This letter puts forward a method for intelligent condition diagnosis of rotating machinery using the probability density analysis and the canonical discriminant analysis (CDA) comprising the following steps. First, the noise is cancelled by statistics filter (SF), and the probability density functions (PDFs) of the vibration signals measured in each state are determined. Second, the segment values of the PDFs of the vibration signals are calculated and the integrated symptom parameters (ISPs) are combined using CDA. Third, Mahalanobis distances between the ISPs are introduced to identify the machine state. Moreover, the selecting discrimination index is optimized according to the accuracy rate of the identification. The efficacy of this novel method was confirmed by the results of the condition diagnosis for a centrifugal blower.

In wireless sensor networks (WSN), the nodes have limited energy resource. So power conservation in individual node is of significant importance. In this paper we derive a cross-layer design method that combines adaptive modulation and coding (AMC) at the physical layer and two sleep modes according to monitoring type at the medium access layer (MAC) in healthcare monitoring WSN, judiciously to maximize spectral efficiency, and to minimize energy consumption, under prescribed error performance constraints. To achieve maximum spectral efficiency in the entire SNR (signal-to-noise ratio) range, we advise a combined transmission mode switching between convolutionally code and unconvolutionally code mode, based which we analyze the energy consumption of the system with multi mobile nodes and a sink node and verify that it is energy efficient to adopt AMC instead of single modulation at physical layer in healthcare monitoring WSN.

Hexagonal structure is different from the traditional square structure for image representation. The geometrical arrangement of pixels on hexagonal structure can be described in terms of a hexagonal grid. Uniformly separating image into seven similar copies with a smaller scale has commonly been used for parallel and accurate image processing on hexagonal structure. However, all the existing hardware for capturing image and for displaying image are produced based on square architecture. It has become a serious problem affecting the advanced research based on hexagonal structure. Furthermore, the current techniques used for uniform separation of images on hexagonal structure do not coincide with the rectangular shape of images. This has been an obstacle in the use of hexagonal structure for image processing. In this paper, we briefly review a newly developed virtual hexagonal structure that is scalable. Based on this virtual structure, algorithms for uniform image separation are presented. The virtual hexagonal structure retains image resolution during the process of image separation, and does not introduce distortion. Furthermore, images can be smoothly and easily transferred between the traditional square structure and the hexagonal structure while the image shape is kept in rectangle

Spiral Architecture based fractal image compression is proposed in this paper. Perceptually, a new definition of range block and domain block is presented on such enhanced image structure. Compared with the common square image architecture, spiral architecture provides higher fidelity to fractal image compression, which is demonstrated by the experimental results.

The authors propose a relational calculus (RC/S) which uses set comparison and set manipulation operators to replace universal quantifiers and negations. It is argued that compared to the Codd relational calculus (RC), RC/S queries are easier to construct and comprehend. It is proved that the expressive power of RC is equivalent to the expressive power of RC/S, and algorithms for translating an RC query into an RC/S query and vice versa are given. A safe RC/S query is defined as one that has finite output and can be evaluated in finite time. Then a subset of RC/S queries, called RC/S* is defined, and it is proved that RC/S* is safe. RC/S* is compared to the existing largest safe subsets of RC, i.e. the evaluable formulas and the allowed formulas. Algorithms are given to transform any evaluable formula into an RC/S* query, and some RC/S* formulas that are not evaluable are given. RC/S* queries can be directly implemented using a graphical language similar to Query-by-Example (QBE). Two different graphical languages are described that are equivalent to the RC/S* in expressive power, and these languages are compared to QBE.>