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Abstract Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis. While current research focuses mainly on measurement noise, measurement bias remains challenging. This study proposes a novel performance-based fault detection and identification (FDI) strategy for twin-shaft turbofan gas turbine engines and addresses these uncertainties through a first-order Takagi-Sugeno-Kang fuzzy inference system. To handle ambient condition changes, we use parameter correction to preprocess the raw measurement data, which reduces the FDI’s system complexity. Additionally, the power-level angle is set as a scheduling parameter to reduce the number of rules in the TSK-based FDI system. The data for designing, training, and testing the proposed FDI strategy are generated using a component-level turbofan engine model. The antecedent and consequent parameters of the TSK-based FDI system are optimized using the particle swarm optimization algorithm and ridge regression. A robust structure combining a specialized fuzzy inference system with the TSK-based FDI system is proposed to handle measurement biases. The performance of the first-order TSK-based FDI system and robust FDI structure are evaluated through comprehensive simulation studies. Comparative studies confirm the superior accuracy of the first-order TSK-based FDI system in fault detection, isolation, and identification. The robust structure demonstrates a 2%–8% improvement in the success rate index under relatively large measurement bias conditions, thereby indicating excellent robustness. Accuracy against significant bias values and computation time are also evaluated, suggesting that the proposed robust structure has desirable online performance. This study proposes a novel FDI strategy that effectively addresses measurement uncertainties.

Abstract Fetal electrocardiogram signal extraction has very important clinical significance. Currently, most existing fetal electrocardiogram signal extraction algorithms are designed for ideal positive definite linear instantaneous mixed models, but the actual collected observation signals often do not meet the ideal standards. Therefore, the extraction results often have many problems such as inaccurate cycles, low correct recognition rates, resource waste, and time consumption. This article proposes an underdetermined nonlinear bounded component analysis method. Firstly, the observed signal is processed using multivariate nonlinear chirp mode decomposition, and then the signal is reconstructed to convert the original underdetermined situation into positive definite; Then, Gaussian technique is used for nonlinear compensation to convert the nonlinear model into a linear model; Finally, the signal is extracted using the bounded component analysis method, which has more relaxed constraint assumptions and does not require signal sources to be independent of each other. This article investigates the role of underdetermined nonlinear bounded component analysis methods in extracting fetal electrocardiogram signals. The experimental dataset mainly uses MIT-BIH, ADFECGD, and PCCDB. The experimental results show that the average similarity coefficient, MSE and SIR values of the algorithm are 0.969, 0.023 and 12.85, respectively, which proves that it can quickly and effectively extract clearer fetal electrocardiogram signals.

Abstract Mental health after acute myocardial infarction (AMI) influences the prognosis of patients. Resilience may contribute to improving a patient’s mental health. However, no study has investigated resilience and its associated factors in young and middle-aged patients undergoing emergency percutaneous coronary intervention (PCI) after the first AMI. This study aimed to identify critical associated factors influencing resilience in these patients. This cross-sectional study recruited 161 young and middle-aged patients with first-episode AMI using a purposive sampling method. These patients were assessed 48 h after emergency PCI using the General Information Questionnaire, the Connor—Davidson Resilience Scale—10, the Perceived Social Support Scale, the General Self-Efficacy Scale, and the Post-traumatic Stress Disorder Scale Civilian Version. Stepwise and logistic regression were conducted to analyze the factors influencing resilience. Receiver operating characteristics (ROC) were used to compare the area under the curves (AUC) for each indicator. The resilience of the 161 participants was 29.50 ± 4.158. Monthly household income, self-efficacy, social support, and post-traumatic stress disorder explained 51.4% of the variance in resilience. Self-efficacy (OR 0.716, CI 0.589–0.870, P

Abstract In the context of the proliferated evolution of network service types and the expeditious augmentation of network resource deployment, the requisition for copious labeled datasets to facilitate superior performance in traffic classification methods, particularly those hinging on deep learning, is imperative. Nonetheless, the procurement and annotation of such extensive datasets necessitate considerable temporal and human resource investments. In response to this predicament, this work introduces a methodology, termed MTEFU, leveraging a deep learning model-based multi-task learning algorithm, strategically designed to mitigate the reliance on substantial labeled training samples. Multiple classification tasks, encompassing duration, bandwidth size, and business traffic category, are incorporated, with a shared parameter strategy implemented amongst tasks to assure the transference of information across disparate tasks. Employing CNN, SAE, GRU, and LSTM as multi-task learning classification models, training validation and experimental testing were conducted on the QUIC dataset. A comparative analysis with single-task and ensemble learning methods reveals that, in the context of predicting network traffic types, the accuracy derived from the multi-task learning strategy, even with a mere 150 labeled samples, can emulate the 94.67% accuracy achieved through single-task learning with a fully labeled dataset of 6139 samples.

As the volume of Chinese electronic medical records (EMRs) experiences an explosive growth, the application of clinical named entity recognition (CNER) technology becomes crucial for the effective utilization of EMR data and practical implementation of evidence-based medicine. While mainstream models excel in capturing global contextual information, their feature extraction mechanisms tend to be unidimensional, limiting their information retrieval capabilities. To address this issue, this paper proposes a Dual-Channel Model for CNER (DCM-CNER), which enhances feature extraction through the introduction of a customized convolutional stack, denoted as emConvNet (embedded ConvNet), for local feature extraction. The model further addresses the vanishing gradient issue by employing a newly designed Gated Dilated Convolutional Neural Network (GDCNN) module with a residual structure. Sequential and contextual information is extracted from the text using a Bidirectional Long Short-Term Memory (BiLSTM) layer. Additionally, a multi-head bilinear attention mechanism is introduced for parallel dynamic feature fusion. Experimental results demonstrate the superiority of the proposed DCM-CNER model, in comparison to the existing mainstream models and state-of-the-art models, achieving F1 scores of 94.15%, 85.26%, and 84.21% on the CCKS2017, CCKS2019, and CLUENER2020 datasets, respectively, thereby validating its effectiveness in performing the task of Chinese CNER in EMRs.

Knowledge graph embedding (KGE) has been identified as an effective method for link prediction, which involves predicting missing relations or entities based on existing entities or relations. KGE is an important method for implementing knowledge representation and, as such, has been widely used in driving intelligent applications w.r.t. question-answering systems, recommendation systems, and relationship extraction. Models based on convolutional neural networks (CNNs) have achieved good results in link prediction. However, as the coverage areas of knowledge graphs expand, the increasing volume of information significantly limits the performance of these models. This article introduces a triple-attention-based multi-channel CNN model, named ConvAMC, for the KGE task. In the embedding representation module, entities and relations are embedded into a complex space and the embeddings are performed in an alternating pattern. This approach helps in capturing richer semantic information and enhances the expressive power of the model. In the encoding module, a multi-channel approach is employed to extract more comprehensive interaction features. A triple attention mechanism and max pooling layers are used to ensure that interactions between spatial dimensions and output tensors are captured during the subsequent tensor concatenation and reshaping process, which allows preserving local and detailed information. Finally, feature vectors are transformed into prediction targets for embedding through the Hadamard product of feature mapping and reshaping matrices. Extensive experiments were conducted to evaluate the performance of ConvAMC on three benchmark datasets compared with state-of-the-art (SOTA) models, demonstrating that the proposed model outperforms all compared models across all evaluation metrics on two of the datasets, and achieves advanced link prediction results on most evaluation metrics on the third dataset.

The traditional Satcom-on-the-Move (SOTM) mechanical structure consists of a dual-axis configuration with an azimuth axis and a pitch axis. In this structure, when the pitch angle is 90 degrees, the rotation of the azimuth axis cannot change the antenna’s direction. To solve this issue, a three-axis SOTM mechanical structure has been developed. The traditional three-axis SOTM servo control system adopts a closed-loop control scheme. In this scheme, due to the difficulty in directly obtaining the antenna’s rotation angle, the angles of rotation for each axis are typically selected to represent the antenna’s rotation angle. The closed-loop feedback includes the angles and angular velocities of the axes, which cannot completely capture the antenna’s motion state, essentially constituting an indirect closed-loop control. Addressing the shortcomings of this indirect closed-loop control, this paper first establishes the kinematic relations between the axes of the three-axis SOTM antenna using the Denavit–Hartenberg (DH) method. Subsequently, the relationship between antenna pointing and the rotational states of the three axes was derived using the Jacobian operator. Building upon this foundation, a direct closed-loop control structure for a three-axis SOTM antenna was designed. To enable the control system to achieve rapid convergence with minimal overshoot, an Active Disturbance Rejection Control (ADRC) algorithm based on smooth continuous functions is introduced as the inner and outer loop controller algorithms within the direct closed-loop control structure. To address the nonlinearity in the design scheme, a piecewise linearization method is proposed to reduce the demands on the microprocessor’s performance and enhance the engineering feasibility of the solution. Finally, the effectiveness of the proposed approach is validated through experiments. The experimental results demonstrate that compared to traditional indirect closed-loop control methods, utilizing the direct closed-loop control method for the three-axis SOTM antenna presented in this paper can lead to higher precision in pointing the antenna towards satellites and enhance communication effectiveness.

The precise monitoring of bearings is crucial for the timely detection of issues in rotating mechanical systems. However, the high complexity of the structures makes the paths of vibration signal transmission exceedingly intricate, posing significant challenges in diagnosing aero-engine bearing faults. Therefore, a Rotational-Spectrum-informed Scale-aware Robustness (RSSR) neural network is proposed in this study to address intricate fault characteristics and significant noise interference. The RSSR algorithm amalgamates a scale-aware feature extraction block, a non-activation convolutional network, and an innovative channel attention block, striking a balance between simplicity and efficacy. We provide a comprehensive analysis by comparing traditional CNNs, transformers, and their respective variants. Our strategy not only elevates diagnostic precision but also judiciously moderates the network’s parameter count and computational intensity, mitigating the propensity for overfitting. To assess the efficacy of our proposed network, we performed rigorous testing using two complex, publicly available datasets, with additional artificial noise introductions to simulate challenging operational environments. On the noise-free dataset, our technique increased the accuracy by 5.11% on the aero-engine dataset compared with the current mainstream methods. Even under maximal noise conditions, it enhances the average accuracy by 4.49% compared with other contemporary approaches. The results demonstrate that our approach outperforms other techniques in terms of diagnostic performance and generalization ability.

Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data privacy. As data privacy becomes more important, it becomes difficult to collect data from multiple data owners to make machine learning predictions due to the lack of data security. Data is forced to be stored independently between companies, creating “data silos”. With the goal of safeguarding data privacy and security, the federated learning framework greatly expands the amount of training data, effectively improving the shortcomings of traditional machine learning and deep learning, and bringing AI algorithms closer to our reality. In the context of the current international data security issues, federated learning is developing rapidly and has gradually moved from the theoretical to the applied level. The paper first introduces the federated learning framework, analyzes its advantages, reviews the results of federated learning applications in industries such as communication and healthcare, then analyzes the pitfalls of federated learning and discusses the security issues that should be considered in applications, and finally looks into the future of federated learning and the application layer.

Abstract Background Glioblastoma multiforme (GBM) is a fatal malignant primary brain tumor in adults. The therapeutic efficacy of chemotherapeutic drugs is limited due to the blood-brain barrier (BBB), poor drug targeting, and short biological half-lives. Multifunctional biomimetic nanodrugs have great potential to overcome these limitations of chemotherapeutic drugs. Methods We synthesized and characterized a biomimetic nanodrug CMS/PEG-DOX-M. The CMS/PEG-DOX-M effectively and rapidly released DOX in U87 MG cells. Cell proliferation and apoptosis assays were examined by the MTT and TUNEL assays. The penetration of nanodrugs through the BBB and anti-tumor efficacy were investigated in the orthotopic glioblastoma xenograft models. Results We showed that CMS/PEG-DOX-M inhibited cell proliferation of U87 MG cells and effectively induced cell apoptosis of U87 MG cells. Intracranial antitumor experiments showed that free DOX hardly penetrated the BBB, but CMS/PEG-DOX-M effectively reached the orthotopic intracranial tumor through the BBB and significantly inhibited tumor growth. Immunofluorescence staining of orthotopic tumor tissue sections confirmed that nanodrugs promoted apoptosis of tumor cells. This study developed a multimodal nanodrug treatment system with the enhanced abilities of tumor-targeting, BBB penetration, and cancer-specific accumulation of chemotherapeutic drugs by combining chemotherapy and photothermal therapy. It can be used as a flexible and effective GBM treatment system and it may also be used for the treatment of other central nervous systems (CNS) tumors and extracranial tumors.

The aim of this study was to develop a predictive model for severe thrombocytopenia after transfemoral transcatheter aortic valve replacement (TAVR). A total of 155 patients treated with TAVR at our center were retrospectively enrolled in this study. The incidence of severe thrombocytopenia after TAVR was 25.16%, and most patients suffered from severe thrombocytopenia on 4 days after procedure. Multivariate regression analysis showed that weight

To overcome the possible gimbal lock problem of the dual-axis satcom-on-the-move (SOTM) antenna, a three-axis tracking satellite SOTM antenna structure appears. The three-axis SOTM antenna is realized by adding a roll axis to the azimuth axis and pitch axis in the dual-axis SOTM structure. There is coupling among the azimuth axis, pitch axis and roll axis in the mechanical structure of the three-axis SOTM antenna, which makes the kinematic modeling of the antenna difficult. This paper introduces a three-axis SOTM antenna kinematic modeling method based on the modified Denavit–Hartenberg (MDH) method, named the new modified Denavit–Hartenberg (NMDH) method. In order to meet the modeling requirements of the MDH method, the NMDH method adds virtual coordinate systems and auxiliary coordinate systems to the three-axis SOTM antenna and obtains the kinematic model of the three-axis SOTM antenna. During the motion of the carrier, the SOTM antenna needs to adjust the antenna pointing in real time according to the changes of the location and attitude of the moving carrier. Therefore, this paper designs a servo control system based on the active disturbance rejection controller (ADRC), introducing a smooth and continuous ADRC fal function to enhance the tracking speed of the servo control system and reduce the overshoot of the output response. Finally, system experiments were carried out with a 60 cm caliber three-axis SOTM antenna. The experiment results show that the proposed servo control method achieves higher antenna tracking satellite accuracy and better communication effects.

This research introduces the Enhanced Scale-Aware efficient Transformer (ESAE-Transformer), a novel and advanced model dedicated to predicting Exhaust Gas Temperature (EGT). The ESAE-Transformer merges the Multi-Head ProbSparse Attention mechanism with the established Transformer architecture, significantly optimizing computational efficiency and effectively discerning key temporal patterns. The incorporation of the Multi-Scale Feature Aggregation Module (MSFAM) further refines 2 s input and output timeframe. A detailed investigation into the feature dimensionality was undertaken, leading to an optimized configuration of the model, thereby improving its overall performance. The efficacy of the ESAE-Transformer was rigorously evaluated through an exhaustive ablation study, focusing on the contribution of each constituent module. The findings showcase a mean absolute prediction error of 3.47∘R, demonstrating strong alignment with real-world environmental scenarios and confirming the model’s accuracy and relevance. The ESAE-Transformer not only excels in predictive accuracy but also sheds light on the underlying physical processes, thus enhancing its practical application in real-world settings. The model stands out as a robust tool for critical parameter prediction in aero-engine systems, paving the way for future advancements in engine prognostics and diagnostics.

Airborne speech enhancement is always a major challenge for the security of airborne systems. Recently, multi-objective learning technology has become one of the mainstream methods of monaural speech enhancement. In this paper, we propose a novel multi-objective method for airborne speech enhancement, called the stacked multiscale densely connected temporal convolutional attention network (SMDTANet). More specifically, the core of SMDTANet includes three parts, namely a stacked multiscale feature extractor, a triple-attention-based temporal convolutional neural network (TA-TCNN), and a densely connected prediction module. The stacked multiscale feature extractor is leveraged to capture comprehensive feature information from noisy log-power spectra (LPS) inputs. Then, the TA-TCNN adopts a combination of these multiscale features and noisy amplitude modulation spectrogram (AMS) features as inputs to improve its powerful temporal modeling capability. In TA-TCNN, we integrate the advantages of channel attention, spatial attention, and T-F attention to design a novel triple-attention module, which can guide the network to suppress irrelevant information and emphasize informative features of different views. The densely connected prediction module is used to reliably control the flow of the information to provide an accurate estimation of clean LPS and the ideal ratio mask (IRM). Moreover, a new joint-weighted (JW) loss function is constructed to further improve the performance without adding to the model complexity. Extensive experiments on real-world airborne conditions show that our SMDTANet can obtain an on-par or better performance compared to other reference methods in terms of all the objective metrics of speech quality and intelligibility.

Currently, the widely used blind source separation algorithm is typically associated with issues such as a sluggish rate of convergence and unstable accuracy, and it is mostly suitable for the separation of independent source signals. Nevertheless, source signals are not always independent of one another in practical applications. This paper suggests a blind source separation algorithm based on the bounded component analysis of the enhanced Beetle Antennae Search algorithm (BAS). Firstly, the restrictive assumptions of the bounded component analysis method are more relaxed and do not require the signal sources to be independent of each other, broadening the applicability of this blind source separation algorithm. Second, the objective function of bounded component analysis is optimized using the improved Beetle Antennae Search optimization algorithm. A step decay factor is introduced to ensure that the beetle does not miss the optimal point when approaching the target, improving the optimization accuracy. At the same time, since only one beetle is required, the optimization speed is also improved. Finally, simulation experiments show that the algorithm can effectively separate independent and dependent source signals and can be applied to blind source separation of images. Compared to traditional blind source separation algorithms, it has stronger universality and has faster convergence speed and higher accuracy compared to the original independent component analysis algorithm.

Currently, most control systems of the aero-engines possess a central controller. The core tasks for the control system, such as control law calculations, are executed in this central controller, and its performance and reliability greatly impact the entire control system. This paper introduces a control system design named Software Defined Control Systems (SDCS), which features a controller-decentralized architecture. In SDCS, a network composed of a set of nodes serves as the controller, so there is no central controller in the system, and computations are distributed throughout the entire network. Since the controller is decentralized, there is a need for decentralized control tasks. To address this, this paper introduces a method for designing decentralized control tasks using periodic linear iteration. Each node in the network periodically broadcasts its own state and updates its next-step state as a weighted sum of its current state and the received current states of other nodes in the network. Each node in the network acts as a linear dynamic controller and maintains an internal state through information exchange with other nodes. We modeled the decentralized controller and obtained the model of the entire control system, and the workload of each obtained decentralized control task is balanced. Then, we obtained a parameter tuning method for each decentralized controller node based on Linear Matrix Inequalities (LMI) to stabilize the closed-loop system. Finally, the effectiveness of the proposed method was verified through digital simulation.

Aiming at engine health management, a novel hybrid prediction method is proposed for exhaust gas temperature (EGT) prediction of gas turbine engines. This hybrid model combines a nonlinear autoregressive with exogenous input (NARX) model and a moving average (MA) model. A feature attention mechanism-enhanced long short-term memory network (FAE-LSTM) is first developed to construct the NARX model, which is used for identifying the aircraft engine using condition parameters and gas path measurement parameters that correlate to the EGT. A vanilla LSTM is then used for constructing the MA model, which is used for improving the difference between the actual EGT and the predicted EGT given by the NARX model. The proposed method is evaluated using real flight process data and compared to several dynamic prediction techniques. The results show that our hybrid model reduces the predicted RMSE and MAE by at least 13.23% and 18.47%, respectively. The developed FAE-LSTM network can effectively deal with dynamic data. Overall, the present work demonstrates a promising performance and provides a positive guide for predicting engine parameters.

Abstract Background Many studies have reported the predictive value of the atherogenic index of plasma (AIP) in the progression of atherosclerosis and the prognosis of percutaneous coronary intervention (PCI). However, the utility of the AIP for prediction is unknown after PCI among type 2 diabetes mellitus (T2DM). Methods 2356 patients with T2DM who underwent PCI were enrolled and followed up for 4 years. The primary outcome was major cardiovascular and cerebrovascular adverse events (MACCEs), considered to be a combination of cardiogenic death, myocardial infarction, repeated revascularization, and stroke. Secondary endpoints included all-cause mortality, target vessel revascularization (TVR), and non-target vessel revascularization (non-TVR). Multivariate Cox proportional hazards regression modelling found that the AIP was correlated with prognosis and verified by multiple models. According to the optimal cut-off point of the ROC curve, the population was divided into high/low-AIP groups. A total of 821 pairs were successfully matched using propensity score matching. Then, survival analysis was performed on both groups. Results The overall incidence of MACCEs was 20.50% during a median of 47.50 months of follow-up. The multivariate Cox proportional hazards regression analysis before matching suggested that the AIP was an independent risk factor for the prognosis of T2DM after PCI (hazard ratio [HR] 1.528, 95% CI 1.100–2.123, P = 0.011). According to the survival analysis of the matched population, the prognosis of the high AIP group was significantly worse than that of the low AIP group (HR (95% CI) 1.614 (1.303–2.001), P

Aero-engine control systems generally adopt centralized or distributed control schemes, in which all or most of the tasks of the control system are mapped to a specific processor for processing. The performance and reliability of this processor have a significant impact on the control system. Based on the aero-engine distributed control system (DCS), we propose a decentralized controller scheme. The characteristic of this scheme is that a network composed of a group of nodes acts as the controller of the system, so that there is no core control processor in the system, and the computation is distributed throughout the entire network. An LQR output feedback control is constructed using system input and output, and the control tasks executed on each node in the decentralized controller are obtained. The constructed LQR output feedback is equivalent to the optimal LQR state feedback. The primal-dual principle is used to tune the parameters of each decentralized controller. The parameter tuning algorithm is simple to calculate, making it conducive for engineering applications. Finally, the proposed scheme was verified by simulation. The simulation results show that a high-precision feedback gain matrix can be obtained with a maximum of eight iterations. The parameter tuning algorithm proposed in this paper converges quickly during the calculation process, and the constructed output feedback scheme achieves equivalent performance to the state feedback scheme, demonstrating the effectiveness of the design scheme proposed in this paper.

ObjectivesCongenitally corrected transposition of the great arteries (ccTGA) is a rare and complex congenital heart disease with the characteristics of double discordance. Enormous co-existed anomalies are the culprit of prognosis evaluation and clinical decision. We aim at delineating a novel ccTGA clustering modality under human phenotype ontology (HPO) instruction and elucidating the relationship between phenotypes and prognosis in patients with ccTGA.MethodsA retrospective review of 270 patients diagnosed with ccTGA in Fuwai hospital from 2009 to 2020 and cross-sectional follow-up were performed. HPO-instructed clustering method was administered in ccTGA risk stratification. Kaplan-Meier survival, Landmark analysis, and cox regression analysis were used to investigate the difference of outcomes among clusters.ResultsThe median follow-up time was 4.29 (2.07–7.37) years. A total of three distinct phenotypic clusters were obtained after HPO-instructed clustering with 21 in cluster 1, 136 in cluster 2, and 113 in cluster 3. Landmark analysis revealed significantly worse mid-term outcomes in all-cause mortality (p = 0.021) and composite endpoints (p = 0.004) of cluster 3 in comparison with cluster 1 and cluster 2. Multivariate analysis indicated that pulmonary arterial hypertension (PAH), atrioventricular septal defect (AVSD), and arrhythmia were risk factors for composite endpoints. Moreover, the surgical treatment was significantly different among the three groups (p < 0.001) and surgical strategies had different effects on the prognosis of the different phenotypic clusters.ConclusionsHuman phenotype ontology-instructed clustering can be a potentially powerful tool for phenotypic risk stratification in patients with complex congenital heart diseases, which may improve prognosis prediction and clinical decision.

To improve the thermoelectric conversion efficiency of solar thermoelectric power, a concentration solar thermoelectric generator (CTEG) unit based on concentrating and storing energy is designed. A Fresnel lens is used to concentrate thermal energy, and a phase change material (PCM) is used to store thermal energy to increase the temperature difference between the hot and cold ends of thermoelectric (TE) sheets. The heat stored in the PCM container will help to generate continuous solar energy at night and improve the thermal power conversion efficiency of the TEGs. The energy conversion equilibrium equation is established for the CTEG unit. By numerical calculation, we conclude that the absorption rate of the coating surface is reduced by 0.1 and the maximum thermoelectric conversion efficiency is reduced by approximately 0.4%. By selecting different heights of the heat exchanger, the unit supplies 1.025 v, 0.4204 v and 0.299 v open circuit voltages on average for approximately 30, 44 and 53 minutes, respectively. Therefore, the height of the heat exchanger affects the rate of heat energy absorption and release. The reversible operation of the CTEG-PCM unit is conducive to the day and night operation of solar power generation and is more suitable to solving the self-supplying power problem for wireless temperature sensors in forests.

The dynamic mechanism of water droplet formation has recently drawn considerable attention in research and application toward silicon rubber composite insulators. This paper is aimed at numerical simulations and experiments on the dynamic mechanism of water droplet formation with different applied voltages and droplet distribution and induced surface discharges on the insulator surface under the impact of AC electric field. The computational fluid dynamics model and experimental setup of multiple droplets were established to analyze the dynamic behavior of droplets and flashover characteristics. The obtained results reveal that the fusion first occurs between larger size droplets and the fusion is hard to happen along with the rise of the fractal dimension (FD) of droplet distribution. The elongation of droplets merges with more droplets and finally affects the electric field distribution on the insulator surface. With the increase of FD, the maximum electric field intensity shows an increasing tendency. Meanwhile, the experimental results show that with the increase of electric field intensity, the droplet fusion phenomenon becomes more obvious, which is consistent with the simulation results. The flashover voltage on the surface of silicon rubber decreases with the decrease of the FD of droplets, indicating that the complexity of droplet distribution has a significant effect on the flashover voltage.

A floating power generation device is designed and fabricated to overcome the power supply limitations of wireless sensor networks for environmental monitoring. Once there is a temperature difference between the upper surface exposed to sunlight and the lower surface in the water, the device is capable of generating power while floating in the wetland environment. Fresnel lenses were applied to concentrate solar irradiation on a selective absorbing coat. Meanwhile two vertical axis rotors were used to cool the cold side of the thermoelectric power generator by catching the breeze. The effects of solar irradiation, temperature distribution, load resistance, wind speed, the maximum power and the electrical efficiency of the thermoelectric power generator were analyzed. When subjected to solar irradiation of 896.38 W/m2, the device generated a potential difference of 381.03 mV and a power output of 8.86 mW via thermoelectric generation. In addition, compared with the system without wind, the output power was increased by approximately 10.96% in our system. The low power wireless networks, used in wetland environments, could be operated by the thermoelectric power generated by the floating device. Besides, this system offers powering solution for self-power miniature devices that are applied in aqueous environment.

Large quantities of volatile organic compounds (VOCs) are released from heat-treated bamboo during the manufacturing process of recombinant bamboo, which affects the environment and human health. In this study, bamboo was treated at 150 °C, 180 °C, and 210 °C for 3 h, and VOCs were collected every hour using a Tenax tube. The VOCs were analyzed with gas chromatography-mass spectrometry (GC-MS) to explore the effect of temperature and time on weight loss ratio, main components, and their relative proportions. The results showed that temperature considerably influenced weight loss ratio, and weight loss ratio increased rapidly at high temperature. Massive quantities of VOC were emitted during the first hour of treatment, and emissions decreased as the time and temperature increased. Terpenes were the primary component of the VOC emissions. Temperature and time exhibited minimal effects on the type of primary components, and the relative proportion of some components exhibited negligible changes over a range of temperatures and times.

To modify the acoustic properties of dawn redwood (Metasequoia glyptostroboides Hu et Cheng), thermal, ultrasonic, and combined thermal-ultrasound treatments were employed in this work. The changes in the logarithmic decrement (δ), specific Young’s modulus (E’/ρ), and extractives content were examined. The results showed that thermal and ultrasonic treatments were both essential for a decrease in the logarithmic decrement (δ) and an increase in the specific Young’s modulus (E’/ρ) of dawn redwood (Metasequoia glyptostroboides Hu et Cheng). A superposition effect on decreasing of the logarithmic decrement (δ) was discovered after thermal-ultrasound combined treatment. The amounts of extractives, extracted by 95 °C distilled water, 1% NaOH solution, and benzene-ethanol solution, decreased after thermal and ultrasonic treatments. Moreover, there was a linear correlation between logarithmic decrement (δ) and extractives, in which extractives from 1% NaOH solution and 95 °C distilled water had a significant effect. Finally, dawn redwood samples treated with ultrasonic power at 340 W for 9 min at a thermal temperature of 200 °C were chosen as the optimal method in this research.

Wettability has become one of the most important factors influencing power equipment insulation system in coastal power grid, which can seriously threaten the safety of power grid. This paper aimed at numerical simulation and experimental investigation on dynamic formation mechanism of water droplet and induced surface discharges on silicone rubber composites under different AC voltages, considering the factors of surface tension and droplet volume. Obtained results reveal that the spreading coefficient increases with increasing the electric field intensity from 1 to 5 kV/cm and with increasing the droplet volume from 10 to 50 µL. The spreading coefficient shows a decreasing tendency when the surface tension increases from 29.3 to 61.0 mN/m. Moreover, the maximum of surface electric field shows an increasing tendency under the condition of increasing both the droplet volume and the surface tension. On the basis of the physical model of droplet deformation, the electric field force and the surface tension play a significant role in the process of droplet deformation. Meanwhile, the experimental results are consistent with the simulation analysis, reflecting that the droplet deformation and the induced surface discharges mainly depend on two parameters: the droplet volume and the magnitude of applied voltage.

For the purpose of collecting solar radiation for energy conversion and utilization and improving the output performance of thermoelectric power-generation components, a new solar thermoelectric conversion device based on an all-glass solar heat transfer pipe and gravity-assisted heat pipe with recycling air cooling and water cooling circuits is designed. The uniqueness of the device lies in the combination of gravity-assisted heat pipes with excellent thermal conductivity and a direct air-cooled mode, a fin-cooled mode, and two solar-driven water-cooling modes with different flow rates. Based on the structure, the device can realize four separate output modes and multiple composite output modes and has practical significance for meeting different load power requirements, such as wireless sensors and electronics. Under a state of regular illumination from 3.14 × 104 lx to 10.04 × 104 lx, with one thermoelectric power generator (TEG) in one mode, the peak output voltage and power values of the device in single-output mode range from 183.1 mV to 370.7 mV and 33.5 mW to 137.2 mW, respectively, proving the feasibility of the proposed device. The energy supply of the above structure is completely obtained from the natural environment, and this aspect provides a high reference value for the cross-research of natural environment energy utilization and thermoelectric energy-conversion technology.

A two-phase closed thermosyphon is an efficient heat transfer element. The heat transfer process of this type of thermosyphon includes conduction and convective heat transfer accompanied by phase changes. Variations in the inclination angle of a thermosyphon affect the steady-state heat transfer performance of the device. Therefore, the inclination angle is an important factor affecting the performance of a thermosyphon. In this paper, an equation for the actual heating area variations with respect to the inclination angle is deduced, and a model for the areal thermal resistance of a thermosyphon is proposed by analyzing the main influence mechanisms of the inclination angle on the heat transfer process. The experimental results show that the areal thermal resistance, which accounts for the effect of the actual heating area, does not change with respect to the inclination angle and exhibits a linear relationship with the heat transfer rate. The thermal resistance equation is fit according to the experimental data when the inclination angle of the thermosyphon is vertically oriented (90°), and the predicted values of the thermosyphon’s thermal resistance are obtained when the thermosyphon is inclined. The deviations between the experimental data and predicted values are less than ±0.05. Therefore, the theoretical equation can accurately predict the thermosyphon’s thermal resistance at different inclination angles.

To efficiently harvest environmental micro-energy from shallow soil, simulated analysis, theoretical arithmetic and experimental verification are performed to explore the spatiotemporal rules of heat transfer on a soil/finned tube interface. Simulations are carried out for 36 types of different working conditions, and the empirical formulas for temperature and heat flux are obtained. The temperature and heat flux can be calculated using the formulas if the soil temperature, soil moisture content and finned tube initial temperature are known. The simulations also show that the highest heat flux can reach approximately 0.30 mW/mm2, and approximately 1507.96 mW of energy can be harvested through the finned tube. Theoretical arithmetic indicates that the heat transfer rate of the copper finned tube is 76.77% higher than that of the bare tube, the highest rate obtained in any study to date. Results also show that the finned tube should be placed where the soil moisture is greater than 30% to get more heat from the soil. A field experiment is carried out in the city of Harbin in Northeast China, where a thermoelectric power generation device has been installed and temperature data have been monitored for a certain time. The results are in good agreement with those obtained from the simulation analysis. The heat transfer processes and heat transfer steady state on the soil/finned tube interface are revealed in this work and are of great importance for the use of geothermal energy.

Bacterial biofilms are involved in adapting to the complex environment and responsible for bacterial persistent infection. The formation of biofilm is a highly complex process during which multifarious genes work together regularly. In this study, we screened the EZ-Tn5 transposon mutant library aiming at identifying the genes involved in biofilm formation of Aeromonas hydrophila. A total of 24 biofilm-associated genes were identified, and the majority of them encoded proteins related to cell structure, transcription and translation, gene regulation, growth and metabolism. The mutant strain TM90, in which a gene encoding oligopeptidase F, pepF, was disturbed, showed significant upregulation of biofilm formation as compared to the parental strain. Meanwhile, the colony phenotype of TM90 became smaller, more transparent and splendent. The adhesive ability of TM90 to HEp-2 cell was significantly increased compared with the parental strain. Fifty percent lethal dose (LD50) determinations in zebrafish demonstrated that the enhanced-biofilm mutant TM90 was highly attenuated relative to the wild-type strain. In conclusion, the pepF gene is first demonstrated to be a negative factor on biofilm formation and involved in pathogenicity of A. hydrophila.