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This paper presented the thermal analysis of the 4 K cryostat of a self-developed 1.5 T conduction-cooled superconducting magnet in order to assess the cooling without liquid helium. Through a two-stage GM cryocooler and some solid thermal links, the NbTi coils achieved 4 K for superconducting state. Whole-body MRI requirements can be satisfied by the superconducting magnet's electromagnetic design, which has a large bore and a homogenous magnetic field. It will take about 278 hours to reach the lowest temperature of 3.73 K through precooling the 4 K cryostat with liquid nitrogen (77 K). Additionally, the FED method was used to evaluate the 4 K cryostat's temperature distribution, and it was determined that the temperature range of 3.99–4.12 K was enough for the magnet to operate safely. The analysis showed that the 1.5 T liquid-helium-free superconducting magnet was feasible and economical, which will be crucial in preserving helium supplies and lowering healthcare expenses.

This article develops a new trustworthy fault diagnosis (TFD) method based on belief rule base expert system with multisource uncertainty information (BRB-MU) for a vehicle, such as a flying machine. To improve the performance of fault diagnosis for a vehicle, a new BRB-MU is developed, where the influence of the random environment disturbance is addressed by introducing uncertain factors of input characteristics. In BRB-MU, the uncertain factors of input characteristics are calculated by the signal to noise ratio based method. The new developed BRB-MU model aims to handle the following three problems: 1) unbalanced observation data of system states; 2) uncertain expert knowledge and 3) random environment disturbance. The first two problems are solved by the combination of expert knowledge and observation data. In parallel, the third problem is handled by the new introduced uncertain factors based on singular value decomposition in BRB-MU. To quantitatively analyze the influence of the uncertain information to the TFD output, the traceability analysis of the BRB-MU is conducted that can provide support for decision making of vehicle optimization design. It is analyzed based on the modeling traceability and parameters interpretability. To demonstrate the effectiveness of the TFD method, an experiment illustration is conducted.

The determination of the optimal maintenance time can ensure the reliability of the system and reduce the maintenance cost. In this article, to improve the performance of optimal maintenance timing (OMT) determination for complex electromechanical equipment (CEE), a new OMT model with quality factor is developed based on evidential reasoning (ER), where the influence of historical maintenance and historical storage is addressed by introducing maintenance and storage (MS) impact factors (IFs) into the fusion process of ER (ER-MS). In ER-MS, maintenance and storage IFs are accurately defined and then participate in the calculation of evidence reliability. The newly developed ER-MS method aims to handle three problems that exist in engineering practice for OMT determination method for CEE: difficulty in establishing an accurate mathematical model, the influence of historical maintenance and historical storage, and performance analysis of the established model. This article also puts forward a quality factor definition method that combines evidence weight and evidence reliability, where the quality factor turns out to be reasonable. Then, to quantitatively analyze the influence of historical maintenance and historical storage on the performance of CEE, sensitivity analysis of historical factors is carried out based on the transparency and traceability of the ER-MS method. The sensitivity factors obtained can provide an improvement basis for the use and storage of CEE. A case study for an inertial navigation system (INS) is conducted to illustrate the effectiveness of the new OMT model.

Bone marrow mesenchymal stem cells (BMSCs) are suggested as candidates for neurodegeneration therapy by autologous stem cells to overcome the lack of neural stem cells in adults. However, the differentiation of BMSCs into functional neurons is a major challenge for neurotherapy. Herein, a methodology has been proposed to induce functional neuronal differentiation of BMSCs on a conductive three-dimensional graphene framework (GFs) combined with a rotating magnetic field. A wireless electrical signal of about 10 μA can be generated on the surface of GFs by cutting the magnetic field lines based on the well-known electromagnetic induction effect, which has been proven to be suitable for inducing neuronal differentiation of BMSCs. The enhanced expressions of the specific genes/proteins and apparent Ca2+intracellular flow indicate that BMSCs cultured on GFs with 15 min/day rotating magnetic field stimulation for 15 days can differentiate functional neurons without any neural inducing factor. The animal experiments confirm the neural differentiation of BMSCs on GFs after transplantation in vivo, accompanied by stimulation of an external rotating magnetic field. This study overcomes the lack of autologous neural stem cells for adult neurodegeneration patients and provides a facile and safe strategy to induce the neural differentiation of BMSCs, which has potential for clinical applications of neural tissue engineering.

Insufficient annotated data and minor lung lesions pose big challenges for computed tomography (CT)-aided automatic COVID-19 diagnosis at an early outbreak stage. To address this issue, we propose a Semi-Supervised Tri-Branch Network (SS-TBN). First, we develop a joint TBN model for dual-task application scenarios of image segmentation and classification such as CT-based COVID-19 diagnosis, in which pixel-level lesion segmentation and slice-level infection classification branches are simultaneously trained via lesion attention, and individual-level diagnosis branch aggregates slice-level outputs for COVID-19 screening. Second, we propose a novel hybrid semi-supervised learning method to make full use of unlabeled data, combining a new double-threshold pseudo labeling method specifically designed to the joint model and a new inter-slice consistency regularization method specifically tailored to CT images. Besides two publicly available external datasets, we collect internal and our own external datasets including 210,395 images (1,420 cases versus 498 controls) from ten hospitals. Experimental results show that the proposed method achieves state-of-the-art performance in COVID-19 classification with limited annotated data even if lesions are subtle, and that segmentation results promote interpretability for diagnosis, suggesting the potential of the SS-TBN in early screening in insufficient labeled data situations at the early stage of a pandemic outbreak like COVID-19.

Neurodegenerative illnesses are among the most difficult conditions to treat in humans. As mesenchymal stem cells (MSCs) are easily acquired for autologous stem cell transplantation, MSC treatment is regarded as one of the most promising therapeutic options. However, typical growth factor-inducing differentiation techniques make the neural differentiation of MSCs problematic. The conventional electrical stimulation route can induce neural differentiation of MSCs, but external wires and complex devices present great obstacles to clinical treatment. In this study, based on the magnetoelectronic effect, wireless electrical signals generated on the reduced graphene oxide membrane (rGO-M) driven by a rotating magnetic field (RMF) were found to induce neural differentiation of MSCs without the aid of any biological or chemical factor. In vitroexperimental results showed that MSCs on rGO-M stimulated by an RMF at a speed of 400 r min−1for 15 min every day could express neuron-specific genes and proteins, which was significantly boosted after continuous treatment for 15 d. In vivoexperiments in rats confirmed that exogenous MSCs on rGO-M could differentiate into neural cells driven by RMF. The rGO-M-mediated wireless electrical stimulation method provides a practical route for stem cell therapy of neurodegenerative diseases because of the low cost of rGO-M and autologous stem cell sources.

This paper develops a new fault diagnosis and tolerant control framework of sensor failure (SFDTC) for complex system such as rockets and missiles. The new framework aims to solve two problems: The lack of data and the multiple uncertainty of knowledge. In the SFDTC framework, two parts exist: The fault diagnosis model and the output reconstruction model. These two parts of the new framework are constructed based on the new developed belief rule base with power set (BRB-PS). The multiple uncertainty of knowledge can be addressed by the local ignorance and global ignorance in the new developed BRB-PS model. Then, the stability of the developed framework is proved by the output error of the BRB-PS model. For complex system, the sensor state is determined by many factors and experts cannot provide accurate knowledge. The multiple uncertain knowledge will reduce the performance of the initial SDFTC framework. Therefore, in the SFDTC framework, to handle the influence of the uncertainty of expert knowledge and improve the framework performance, a new optimization model with two optimization goals is developed to ensure the smallest output uncertainty and the highest accuracy simultaneously. A case study is conducted to illustrate the effectiveness of the developed framework.

Quality state assessment (QSA) is a top priority to ensure the quality and reliability of complex system repairs. In complex systems, a large number of features can reflect complex nonlinear associations with multiple information sources. However, the current research on the QSA of complex systems based on the evidential reasoning (ER) rule generally ignores the complex relationship between features, increasing maintenance difficulty. Therefore, a novel ER rule-based QSA approach considering feature selection (FS) for a complex system is proposed in this article. The correlation and redundancy factors are defined by introducing the Spearman correlation coefficient and mutual information. A hybrid weight factor is employed to balance the significance of redundancy and relevance, which can measure the correlation and redundancy between features. Second, a hybrid weight coefficient is introduced into the ER rule, and a QSA model considering FS is proposed. Third, a parameter optimization model based on the ER rule is proposed, and the optimized ER rule model can adaptively select informative and balanced features. Finally, an ER rule-based QSA fusion mechanism is put forward to fuse the selected features to obtain the quality state of the complex system. A numerical study is taken out to demonstrate the validity and performance of the approach. Furthermore, an applicable experiment of an inertial navigation system (INS) is introduced to prove the application of the ER rule-based QSA approach.

Methods for automatic image analysis are demanded for dealing with the explosively increased imaging data in clinics. Osteoarthritis (OA) is a typical disease diagnosed based on X-ray imaging. Herein, we propose a novel modeling strategy based on YOLO version 3 (YOLOv3) for automatic simultaneous localization of knee joints and quantification of radiographic knee OA. As an advanced deep convolutional neural network (CNN) algorithm for target detection, YOLOv3 enables simultaneous small object detection and quantification due to its unique residual connection and feature map merging. Hence, a unified CNN model is built for the elegant integration of knee joint detection and corresponding OA severity grading using the YOLOv3 framework. We achieve desirable accuracy in knee OA grading using the public and clinical datasets. It provides improvements in the precision, recall, F1score and diagnostic accuracy of knee OA as well. Because of the fully automatic target detection and quantification, the time of handling an image is merely 40 ms from inputting the image to getting its label, supporting quick clinic decisions. It, thus, affords convenient and efficient image analysis for daily clinical diagnosis.

As sensor readings are used for vehicle flight control, their reliability directly affects flight performance. This article develops a new fault diagnosis and tolerance (FDT) method for sensor failures of vehicles by addressing three problems: unavailable faulty data, difficulty in establishing the analytical system, and the inconsistence of expert cognitive ability. For the purpose, a new belief rule base model with multiexpert joint (BRB-ME) is proposed. The first two problems are handled by combining the small size of observation data and the uncertain knowledge from multiple experts in BRB-ME. For the third problem, a new multiexpert joint strategy is proposed in the BRB-ME model. The experts first construct their own models, and then, the models are fused with different weights according to the experts’ ability, such as research fields and the working time. Then, a new FDT framework is developed based on BRB-ME for detecting vehicles’ sensor failures, where the sensor failures are tolerated by the reconstruction strategy for faulty sensor output. Moreover, in order to address the influence of uncertain expert knowledge, an optimization model is constructed for obtaining the optimal solutions for the framework. An experimental illustration is conducted for accelerometer failure. The diagnosis accuracy is 97.50%, and the developed framework can ensure the navigation accuracy of the vehicle under accelerometer failure.

This article considers the dynamic event-triggered simultaneous fault detection and leader-following consensus (SFDLFC) problem for nonlinear multiagent systems (MASs) with directed graphs. First, based on the distributed fault detection (FD) observer, a distributed dynamic event-triggered consensus controller is designed together with a dynamic event-triggering mechanism (DETM) to achieve leader-following consensus of MASs under directed graphs and lessen the amount of data transmission between neighboring agents. Then, a residual evaluation mechanism is developed under directed graphs to detect and isolate the faulty agent. To make the residual signal sensitive to the faults and robust to the tracking errors, a hybrid $H_{\infty }/H_{-}$ method is used to transform the design problem of consensus controller and FD observer into a multiobjective optimization problem, and sufficient conditions are obtained to satisfy the optimization performance constraints by Lyapunov theory. It should be noted that compared with the existing related works, our main contribution is that the proposed method could achieve consensus and detect faults of MASs under a more general communication graph, and the proposed DETM could generate larger triggering intervals and therefore achieve better resource efficiency. Finally, a simulation result is provided to demonstrate the effectiveness of the proposed technique.

Background: The operation state evaluation and fault location of the transformer is one of the technical bottlenecks restricting the safe power grid operation. Methods: A hybrid intelligent method based on the Improved Sine Cosine Algorithm and BP neural network (ISCA-BP) is developed to improve the accuracy of transformer fault diagnosis. First, the cloud model is introduced into the Sine Cosine Algorithm (SCA) to determine the conversion parameter of each individual to balance the global search and local exploitation capabilities. After that, six popular benchmark functions are used to evaluate the effectiveness of the proposed algorithm. Finally, based on the dissolved gas analysis technology, the improved SCA algorithm is employed to find the optimal weight and threshold parameters of the BP neural network, and the transformer fault classification model is established. Results: Simulation results indicate that the improved SCA algorithm exhibits strong competitiveness. Furthermore, compared with the BP neural network optimized by the Sine Cosine Algorithm (SCA-BP) and BP neural network, the ISCA-BP method can significantly improve the diagnostic accuracy of transformer faults. Conclusion: The proposed intelligent method can provide a valuable reference idea for transformer fault classification.

Mesenchymal stem cells (MSCs) are multi-potent cells that are able to differentiate and mature into various types of cells under a certain microenvironment for cell therapy and tissue regeneration. Scandium (Sc), an important rare earth element, recently has been intensively investigated in biomedical fields as well as industrial engineering, and chloride channels have been proven to be able to affect osteogenic differentiation. Thus, it is significant to investigate effects of ScCl3on cell activities of MSCs. In this paper, rat bone MSCs (rBMSCs) were co-cultured with various concentrations of ScCl3(1 × 10−8, 1 × 10−6, and 1 × 10−4 mol/L) to evaluate their influence on cell proliferation as well as osteogenic and adipogenic differentiation in vitro. The results indicate that ScCl3promotes the proliferation of rBMSCs initially, which is yet reduced upon ion accumulation. We used immunofluorescence staining, quantitative real time polymerase chain reactions, and assays measuring alkaline phosphatase activity, mineralized deposits, and intracytoplasmic lipids to reveal that rBMSCs treated with ScCl3at concentrations of 1 × 10−8–1 × 10−6 mol/L can enhance levels of osteogenic differentiation in a dose-dependent manner and reduce adipogenic differentiation to a certain degree through Wnt/β-catenin signaling pathway. These results indicate that appropriate concentrations of ScCl3can improve osteogenic differentiation in the lineage commitment of rBMSCs, and thus, promote bone remodeling. This study implies that ScCl3possesses great potentials in the treatment of bone diseases and would provide new strategy of designing composites by SiCl3doping for biomedical applications in the future.

For patients with hepatocellular carcinoma, intratumor necrosis at CT was predictive of both macrotrabecular-massive subtype and vessels encapsulating tumor clusters pattern, and a distinct CT imaging phenotype was associated with increased risk of early recurrence after surgical resection.

Salmonellaselectively colonizes into the hypoxic tumor region and exerts antitumor effects via multiple mechanisms, while the tumor colonized Salmonellarecruits host neutrophils into the tumor, presenting a key immunological restraint to compromise the Salmonellaefficacy. Here, we develop a combinatorial strategy by employing silver nanoparticles (AgNPs) to improve the efficacy and biosafety of Salmonella. The AgNPs were decorated with sialic acid (SA) to allow selective recognition of L-selectin on neutrophil surfaces, based on which the tumor-homing of AgNPs was achieved by neutrophil infiltration in the Salmonellacolonized tumor. The tumor-targeting AgNPs exert the functions of (1) local depletion of neutrophils in tumors to boost the efficacy of Salmonella, (2) direct killing tumor cells via L-selectin-mediated intracellular delivery, and (3) clearing the residual Salmonellaafter complete tumor eradication to minimize the side effects. With a single tail vein injection of such combination treatment, the tumor was eliminated with high biosafety, resulting in a superior therapeutic outcome.

The composition of the modern aerospace system becomes more and more complex. The performance degradation of any device in the system may cause it difficult for the whole system to keep normal working states. Therefore, it is essential to evaluate the performance of complex aerospace systems. In this paper, the performance evaluation of complex aerospace systems is regarded as a Multi-Attribute Decision Analysis (MADA) problem. Based on the structure and working principle of the system, a new Evidential Reasoning (ER) based approach with uncertain parameters is proposed to construct a nonlinear optimization model to evaluate the system performance. In the model, the interval form is used to express the uncertainty, such as error in testing data and inaccuracy in expert knowledge. In order to analyze the subsystems that have a great impact on the performance of the system, the sensitivity analysis of the evaluation result is carried out, and the corresponding maintenance strategy is proposed. For a type of Inertial Measurement Unit (IMU) used in a rocket, the proposed method is employed to evaluate its performance. Then, the parameter sensitivity of the evaluation result is analyzed, and the main factors affecting the performance of IMU are obtained. Finally, the comparative study shows the effectiveness of the proposed method.

Safety assessment is one of important aspects in health management. In safety assessment for practical systems, three problems exist: lack of observation information, high system complexity and environment interference. Belief rule base with attribute reliability (BRB-r) is an expert system that provides a useful way for dealing with these three problems. In BRB-r, once the input information is unreliable, the reliability of belief rule is influenced, which further influences the accuracy of its output belief degree. On the other hand, when many system characteristics exist, the belief rule combination will explode in BRB-r, and the BRB-r based safety assessment model becomes too complicated to be applied. Thus, in this paper, to balance the complexity and accuracy of the safety assessment model, a new safety assessment model based on BRB-r with considering belief rule reliability is developed for the first time. In the developed model, a new calculation method of the belief rule reliability is proposed with considering both attribute reliability and global ignorance. Moreover, to reduce the influence of uncertainty of expert knowledge, an optimization model for the developed safety assessment model is constructed. A case study of safety assessment of liquefied natural gas (LNG) storage tank is conducted to illustrate the effectiveness of the new developed model.

As an important supporting material for calligraphy and painting relics in collections, the silk substrate is generally in acidification. And owing to an extremely high light response, it is very prone to different forms of lighting damage, including color and mechanical aspects. Considering that the Light Emitting Diode (LED) with flexible spectral compositions is widely used in the lighting environment of collections, the spectral responsivity of acidified silk substrates is the core issue concerning the long-term conservation of calligraphy and painting relics. In this study, based on the accelerated aging experiment of nine narrowband LEDs with different wavelengths (λ) in the visible range on silk samples with different acidification degrees (pH values), the infrared spectrum was measured non periodically. Processed with the Principal Component Analysis (PCA), the obtained ΔPC1was used to evaluate the lighting damage of silk samples. Based on the damage change curve, its spectral responsivity function, that is, the mathematical relationship between lighting damage and λ and pH values, was further fitted. Combined with the previous research on the spectral responsivity of colorants, an evaluation method for lighting damage was proposed, which is the key to the conservation of calligraphy and painting relics.

As obtaining flexible tactile sensors with high sensitivity, waterproofness and breathability at the same time is still a huge challenge, which limits their application in advanced fields such as human prosthetics and robotic arms. Herein, a fully flexible piezoelectric tactile sensor with high-performance, energy-efficient, waterproof, and breathable was constructed by capacitive coupling the β-PVDF nanofiber membrane (NM) with electro-mechanical conversion function and organic field effect transistor (OFET) device with signal amplification function. The inherent waterproofed β-phase PVDF NM was fabricated by advanced electrospinning process, such morphology with thousands of micropores not only gives the device excellent breathability, but also increases the deformation tolerance and accelerates the respond/recovery time, so that the obtained device can be bent at will without affecting its performance. At the same time, under the guidance of pizotronics, the charge generated by PVDF membrane based on piezoelectric effect polarization was sufficient to drive the OFET device, the as-fabricated sensor shows great sensing performance in monitoring human physiological signals without applying a gate voltage. Such device exhibits excellent pressure sensitivity, detection limit, and response time of 7.94 KPa-1, 48.54 Pa and 105 ms, respectively, which may become promising device in smart electronic skins, human motion detection, and human−machine interaction.

Assessing the health condition of satellite li-ion battery pack accurately is critical for determining the safety of satellite power supply and ensuring that satellite satisfies its service life. Since the limitations of space environment make satellite battery special in operation and maintenance, misjudgments may occur when using existing assessment methods. To solve this problem, a novel health condition assessment indicator system that integrates battery inconsistency and pack performance indicators is built according to the performance requirement of battery pack. Based on the typical strategy of satellite battery pack performance degradation management, equalization and bypass state are employed as factors to make assessment result more accurate and comprehensive. Then, a method based on ER (evidential reasoning) and BRB (belief rule base) is proposed to achieve inconsistencies fusion and rule-based assessment. And the results can be expressed as belief degree distribution or score. Finally, the health condition of a certain type of in-orbit satellite's li-ion battery pack is assessed using telemetry data. The results are consistent with the battery's actual working state, which proves the effectiveness of the proposed method.

A radiomics model based on preoperative contrast-enhanced CT was used to predict the aggressive macrotrabecular-massive subtype of hepatocellular carcinoma and was associated with defective humoral immunity.

Although engineered nanoparticles loaded with specific growth factors are used to regulate differentiation of stem cells, the low loading efficiency and biocompatibility are still great challenges in tissue repair. A nature‐inspired biomimetic delivery system with targeted functions is attractive for enhancing cell activity and controlling cell fate. Herein, a stem cell membrane (SCM)‐wrapped dexamethasone (DEX)‐loaded zeolitic imidazolate framework‐8 (ZIF‐8) is constructed, which integrates the synthetic nanomaterials with native plasma membrane, to achieve efficient DEX delivery and DEX‐mediated bone repair. The DEX@ZIF‐8‐SCM enables high DEX loading capacity, modulates the sustained release, and facilitates the specific uptake of mesenchymal stem cells (MSCs), owing to the porous property of ZIF‐8 and the innate targeting capability of SCM. The endocytosed DEX@ZIF‐8‐SCM shows high cytocompatibility and greatly enhances the osteogenic differentiation of MSCs. Furthermore, RNA‐sequencing data reveal that the phosphoinositide 3‐kinase (PI3K)‐Akt signaling pathways are activated and dominantly involved in the accelerated osteogenesis. In the bone defect model, the administrated DEX@ZIF‐8‐SCM exerts excellent biocompatibility and effectively promotes bone regeneration. Overall, the SCM‐derived biomimetic nanoplatform achieves targeted delivery, excellent biosafety, and enhanced osteogenic differentiation and bone repair, which provides a new and valid strategy for treating various tissue injuries. A biomimetic delivery system is constructed by encapsulating dexamethasone (DEX)‐loaded zeolitic imidazolate framework‐8 (ZIF‐8) with a stem cell membrane (SCM). Owing to the porous structure of ZIF‐8 and the innate targeting capability of SCM, the DEX@ZIF‐8‐SCM enables high DEX loading capacity and specific delivery to stem cells. Furthermore, the nanoplatform exerts superior capacity of enhancing osteogenic differentiation and bone regeneration.

Neural stem cells (NSCs) are thought to be the best seed cells for neurodegenerative diseases therapy due to their potential to differentiate into either neurons or glial cells. However, the lack of resources for autologous NSCs prohibits their clinical applications. Although mesenchymal stem cells can be conveniently obtained from patients, neural differentiation is difficult to be realized, which becomes the great challenge for stem cell-based neurodegenerative diseases therapy. Herein, we report a novel method to induce neural differentiation of rat bone-marrow-derived mesenchymal stem cells (rBMSCs) based on piezotronics without any neural inducing factors (NIFs). In this work, a hybrid nanofibrous membrane was prepared by assembling a layer of FeOOH nanorods on the surface of polyvinylidene fluoride (PVDF) electrospun nanofibers. Under ultrasonic irradiation, piezoelectric-driven localized electric potential and electrical-driven iron ion (Fe3+) release based on piezotronics induced neural differentiation of rBMSCs cultured on the surface of these membranes. Genetic and molecular assays on the differentiated cells demonstrated that the synergistic effects of electrical signals and iron ion release induced the differentiation of rBMSCs into neurons (γ-aminobutyric acid (GABA)ergic, cholinergic and aminergic neurons) without any NIFs. Intracellular calcium imaging experiments showed that the differentiated cells generated fast peaking spontaneous [Ca2+]i-transients under the effects of neurotransmitters, especially GABA, indicating that rBMSCs-derived neurons had neuronal function. This study provides a novel strategy for inducing neural differentiation of rBMSCs via wireless stimulation with piezotronic effect, which is of great significance in clinical and neural tissue engineering.

Islet transplantation is a promising ß-cell replacement therapy for type 1 diabetes, which can reduce glucose lability and hypoglycemic episodes compared with standard insulin therapy. Despite the tremendous progress made in this field, challenges remain in terms of long-term successful transplant outcomes. The insulin independence rate remains low after islet transplantation from one donor pancreas. It has been reported that the islet-related inflammatory response is the main cause of early islet damage and graft loss after transplantation. The production of interleukin-1ß (IL-1ß) has considered to be one of the primary harmful inflammatory events during pancreatic procurement, islet isolation, and islet transplantation. Evidence suggests that the innate immune response is upregulated through the activity of Toll-like receptors and The NACHT Domain-Leucine-Rich Repeat and PYD-containing Protein 3 inflammasome, which are the starting points for a series of signaling events that drive excessive IL-1ß production in islet transplantation. In this review, we show recent contributions to the advancement of knowledge of IL-1ß in islet transplantation and discuss several strategies targeting IL-1ß for improving islet engraftment.

Hidden faults are important characteristics of complex systems that cannot be observed directly. The hidden behavior of a system, such as the health state and safety level, has a direct correlation with these hidden faults. After the predicted hidden behavior reaches a fault boundary, certain measures must be taken to avoid fault occurrences. Thus, hidden faults can be predicted by the hidden behavior of a system. The belief rule base (BRB) has been used to predict hidden behaviors. However, two problems remain to be solved in engineering practice. First, when the observed information is absent, ignorance may exist in the output. If only global ignorance is considered, it may be unreasonable in certain cases, which can influence the prediction model. Second, the effects of disturbance factors such as noise and sensor quality may cause the reliability of the gathered information to decline, which indirectly leads to unreliability of the hidden behavior. Thus, to address the global ignorance and unreliable hidden behavior, a new hidden BRB model with a power set and considering attribute reliability (PHBRB-r) is proposed for hidden fault prediction. In the PHBRB-r model, the effects of disturbance factors on hidden behavior are considered using attribute reliability, and the discernment frame is a power set. A case study of hidden fault prediction is conducted to demonstrate the effectiveness of the PHBRB-r model.