To realize the utilization of peanut shell, this study investigates the combustion behavior, chemical kinetics and thermodynamic parameters of peanut shell using TGA under atmospheric air at the heating rates of 10, 20, and 30 K/min. Results indicate that increasing the heating rate leads to higher ignition, burnout, and peak temperatures, as observed in the TG/DTG curves shifting to the right. Analysis of combustion performance parameters suggest that higher heating rates can enhance combustion performances. Kinetic analysis using two model-free methods, KAS and FWO, shows that the activation energy (Eα) ranges from 93.30 to 109.65 kJ/mol for FWO and 89.72 to 103.88 kJ/mol for KAS. The data fit well with coefficient of determination values (R2) close to 1 and the mean squared error values (MSE) less than 0.006. Pre-exponential factors using FWO range from 2.19 × 106 to 8.08 × 107 s–1, and for KAS range from 9.72 × 105 to 2.25 × 107 s–1. Thermodynamic analysis indicates a low-energy barrier (≤±6 kJ/mol) between activation energy and enthalpy changes, suggesting easy reaction initiation. Furthermore, variations in enthalpy (ΔH), Gibbs free energy (ΔG), and entropy (ΔS) upon conversion (α) suggest that peanut shell combustion is endothermic and non-spontaneous, with the generation of more homogeneous or well-ordered products as combustion progresses. These findings offer a theoretical basis and data support for the further utilization of agricultural biomass.

Sorting out the requirements for intelligent functions is the prerequisite and foundation of the top-level design for the development of intelligent ships. In light of the development of inland intelligent ships for 2030, 2035, and 2050, based on the analysis of the division of intelligent ship functional modules by international representative classification societies and relevant research institutions, eight necessary functional modules have been proposed: intelligent navigation, intelligent hull, intelligent engine room, intelligent energy efficiency management, intelligent cargo management, intelligent integration platform, remote control, and autonomous operation. Taking the technical realization of each functional module as the goal, this paper analyzes the status quo and development trend of related intelligent technologies and their feasibility and applicability when applied to each functional module. At the same time, it clarifies the composition of specific functional elements of each functional module, puts forward the stage goals of China’s inland intelligent ship development and the specific functional requirements of different modules under each stage, and provides reference for the Chinese government to subsequently formulate the top-level design development planning and implementation path of inland waterway intelligent ships.

Non-linear behavior is widespread in many kinds of signals from nature and engineering fields. Although the high energy-concentration level of various advanced time-frequency (TF) analysis (TFA) techniques currently developed ensure a fine representation of non-linear behavior of time-varying component (TVC) of the signal, it is far from sufficient to solely consider the single aspect of energy-concentration level, because the actual signal composition is always more complicated, especially for some thorny difficulties such as strong noise interference and the early weak TVC, etc., these negative factors bring significant challenges to reveal the non-linear behavior of TVC of practical signals. A new TFA method aimed at this issue, called recursive demodulated synchro spline-kernelled chirplet extracting transform (RDSSCET), is proposed in this paper. The proposed RDSSCET is developed on the frame of synchro spline-kernelled chirplet extracting transform (SSCET) and a newly designed external-internal nested double iteration mechanism, which effectively addresses the limitation of SSCET in handling multicomponent signals while also exhibiting superior high energy concentration and noise robustness. As such, the proposed RDSSCET can yield a more favorable outcome when revealing the non-linear behavior of TVC, particularly for weak TVC with strong noise interference. Comparison analysis results in numerical simulations verified the performance of RDSSCET. Its effectiveness in real applications is fully tested via two real-world sound signals and a practical case of wind turbine fault detection.

To ensure the reliable operation of high aluminum steel smelting, it is common practice to employ low-reactivity CaO-Al2O3-based slag. Various analytical methods, including scanning electron microscopy with energy-dispersive X-ray spectroscopy, X-ray diffraction, Raman spectroscopy, and so on, are employed to investigate the impact of Ce2O3 content on the crystalline phase and structure within the CaO-Al2O3-MgO-SiO2 slag. Concurrently, the slag properties of viscosity and melting temperature are assessed. The findings reveal that the principal crystalline phases in the CaO-Al2O3-MgO-SiO2 slag include Ca3Al4MgO10, Ca3Al2O6, and MgO. Additionally, the introduction of Ce2O3 leads to the formation of the high melting temperature CaCeAlO4 phase, whose abundance correlates positively with the Ce2O3 content. Ce2O3 demonstrates efficient bridging oxygen bond disruption, causing the transformation of network former [AlO4]-tetrahedron into network modifier [AlO6]-octahedron. Simultaneously, Q4Al shifts to Q2Al and Q3Al in [AlO4]-tetrahedron, resulting in a marked reduction in degree of polymerization of slag. This reduction is evident in decreased viscosity and melting temperature, reaching the minimum value of 0.153 Pa·s and 1331 °C, respectively. However, the Ce2O3 content is beyond 15 wt%, Q2Al and Q3Al transform into Q4Al, which lead to an upward trend in high-temperature viscosity. Furthermore, as the temperature decreases, the precipitation of the CaCeAlO4 phase from the slag occurs. The content and precipitation temperature of CaCeAlO4 increase with rising Ce2O3 content, representing the primary factor behind the elevation of low-temperature viscosity and melting temperature.

Background and ObjectiveOptical coherence tomography (OCT) is currently one of the most advanced retinal imaging methods. Retinal biomarkers in OCT images are of clinical significance and can assist ophthalmologists in diagnosing lesions. Compared with fundus images, OCT can provide higher resolution segmentation. However, image annotation at the bounding box level needs to be performed by ophthalmologists carefully and is difficult to obtain. In addition, the large variation in shape of different retinal markers and the inconspicuous appearance of biomarkers make it difficult for existing deep learning-based methods to effectively detect them. To overcome the above challenges, we propose a novel network for the detection of retinal biomarkers in OCT images. MethodsWe first address the issue of labeling cost using a novel weakly semi-supervised object detection method with point annotations which can reduce bounding box-level annotation efforts. To extend the method to the detection of biomarkers in OCT images, we propose multiple consistent regularizations for point-to-box regression network to deal with the shortage of supervision, which aims to learn more accurate regression mappings. Furthermore, in the subsequent fully supervised detection, we propose a cross-scale feature enhancement module to alleviate the detection problems caused by the large-scale variation of biomarkers. We also propose a dynamic label assignment strategy to distinguish samples of different importance more flexibly, thereby reducing detection errors due to the indistinguishable appearance of the biomarkers. ResultsWhen using our detection network, our regressor also achieves an AP value of 20.83 s when utilizing a 5 % fully labeled dataset partition, surpassing the performance of other comparative methods at 5 % and 10 %. Even coming close to the 20.87 % result achieved by Point DETR under 20 % full labeling conditions. When using Group R-CNN as the point-to-box regressor, our detector achieves 27.21 % AP in the 50 % fully labeled dataset experiment. 7.42 % AP improvement is achieved compared to our detection network baseline Faster R-CNN. ConclusionsThe experimental findings not only demonstrate the effectiveness of our approach with minimal bounding box annotations but also highlight the enhanced biomarker detection performance of the proposed module. We have included a detailed algorithmic flow in the supplementary material.

In the dual-inertia servo system, the servo resonance in which the characteristics are closely related to the mechanical parameters is usually suppressed by adding a notch filter. As a result, accurate mechanical parameter identification facilitates adjusting the notch parameters and avoiding the failed servo resonance suppression caused by the mechanical parameter mismatch. In this paper, a topological structure expanded by five improved Sliding-Mode Observers (ISMOs) is proposed to simultaneously identify the load speed, the motor inertia, the load inertia, the stiffness coefficient, and the load disturbance, where the load speed and the load disturbance are considered as intermediate variables to provide sufficient load-side information to ensure the identification accuracy of the mechanical parameters. Furthermore, according to the natural knowledge of the dual-inertia servo system, a time-varying interconnected gain with an equal derivative to the identification parameter in each ISMO is designed to promote the global convergence of the topological structure. Finally, the identified mechanical parameters are utilized to adjust the notch parameters for the servo resonance suppression. Simulation and experimental results are presented to indicate the superiority of the topological structure and the effectiveness of the servo resonance suppression.

In practical assembly enterprises, customization and rush orders lead to an uncertain demand environment. This situation requires managers and researchers to configure an assembly line that increases production efficiency and robustness. Hence, this work addresses cost-oriented mixed-model multimanned assembly line balancing under uncertain demand, and presents a new robust mixed-integer linear programming model to minimize the production and penalty costs simultaneously. In addition, a reinforcement learning-based multiobjective evolutionary algorithm (MOEA) is designed to tackle the problem. The algorithm includes a priority-based solution representation and a new task-worker-sequence decoding that considers robustness processing and idle time reductions. Five crossover and three mutation operators are proposed. The Q -learning-based strategy determines the crossover and mutation operator at each iteration to effectively obtain Pareto sets of solutions. Finally, a time-based probability-adaptive strategy is designed to effectively coordinate the crossover and mutation operators. The experimental study, based on 269 benchmark instances, demonstrates that the proposal outperforms 11 competitive MOEAs and a previous single-objective approach to the problem. The managerial insights from the results as well as the limitations of the algorithm are also highlighted.

In this paper, a novel adjustable gap double-side axial flux permanent magnet hysteresis damper (AGDSAPHD) is proposed, of which the torque can be adjusted by changing the air gap and the magnetic field angle between the upper and lower stators at the same time. To analyze and design, a modified vector Jiles-Atherton (JA) hysteresis model is used for simulating the magnetic flux distribution and the hysteresis torque of AGDSAPHD. The eddy currents in the AGDSAPHD are analyzed, which have a great influence on the output torque when the rotational speed is fast. A splitting method used in the rotor of the AGDSAPHD is proposed to reduce the effect of eddy currents. The heat distribution of the AGDSAPHD is calculated to determine its slip power and operating mode. A prototype is built and tested to verify the reliability of the analysis. The experiments prove that the modified vector JA model has high computational accuracy in calculating the hysteresis effects of the AGDSAPHD. A splitting method used in the rotor can greatly reduce the eddy current torque. AGDSAPHD has a greater torque adjustment range and better temperature stability than the traditional one.

Circular RNAs (circRNAs) are critical regulators of lung adenocarcinoma (LA) progression. Although a molecular marker targeting hsa_circ_0000018 has been developed and used for diagnosing colon cancer, the role of this circRNA in LA progression has not been explored till now. This study aimed to elucidate the role and regulatory mechanisms of hsa_circ_0000018 in LA progression. LA tissues and corresponding adjacent non-tumor tissues were collected from 36 patients to confirm the levels of circRNAs, microRNAs (miRNAs), and messenger RNAs (mRNAs) using quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR). We also cultured two LA cell lines (A549, PC-9), and the human normal lung epithelial cell line BEAS-2B. Cell function experiments were conducted to assess malignancy in LA cells, including proliferation, migration, and invasion, following forced hsa_circ_0000018 expression. The correlation between hsa_circ_0000018, let-7f-5p, and family with sequence similarity 96 member A (FAM96A) was confirmed by using starBase (miRNA-circRNA interaction database), luciferase assay, and western blotting. Expression of hsa_circ_0000018 and FAM96A was reduced, whereas that of let-7f-5p was upregulated in LA. Cell function assays revealed that upregulation of hsa_circ_0000018 had a suppressive effect on the proliferation, migration, and invasion of LA cells. Additionally, hsa_circ_0000018 sponge binds let-7f-5p, resulting in upregulation of FAM96A expression. Our data reveal hsa_circ_0000018 as a tumor suppressor in LA that targets the let-7f-5p/FAM96A axis. Our findings enrich the known regulatory network of circRNAs in LA.

To optimize the reliability of solid oxide fuel cells (SOFCs), an analysis method based on automatic spectral clustering and neighborhood rough sets is proposed in this paper. This is the first application of multi-state reliability theory to SOFC systems. Firstly, a state partitioning method based on automatic spectral clustering is proposed to partition the operational data into different classes. Then, the feature extraction method based on the neighborhood rough set is used to find the most sensitive variables to system state changes. Finally, the accuracy of state segmentation and feature extraction is verified by training state classifiers. In addition, the generality of the method is verified by migrating it to another pure hydrogen-fueled SOFC system. The results show that the state segmentation method successfully partitions the electrical characteristics into 10 states. Even the data in the early stage of failure can be well segmented. Moreover, the feature extraction method extracts 6 variables that are most sensitive to state changes, which reduces the number of the variables by 85.7%. The state classifier can achieve over 94% correct state recognition rate within the 30s. Meanwhile, the method has good generality and transferability.