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Structural health monitoring (SHM) systems have been widely applied in long-span bridges and a large amount of SHM data is continually collected. The harsh environment of sensors installed at structures causes multiple types of anomalies such as outlier, minor, missing, trend, drift, and break in the SHM data, which seriously hinders the further analysis of SHM data. In order to achieve anomaly detection from a large amount of SHM data, this paper proposes a long-short term memory (LSTM) network-based anomaly detection method. Firstly, the proposed method reduces the workload for preparing training sets. Secondly, the purpose of real-time anomaly detection can be met. Thirdly, the problem of high alarm rate can be avoided by utilizing double thresholds. To validate the effectiveness of the proposed method, a case study of finite element model simulation is firstly introduced, which illustrates the detailed implementation process. Finally, acceleration data from the SHM system of a long-span suspension bridge located in Jiangyin, China is employed. The results show that the proposed method can detect anomaly with high accuracy and identify abnormal accidents such as a ship collision quickly.

The paper proposes a biomass cross-upgrading process that combines hydrothermal carbonization and pyrolysis to produce high-quality blast furnace injection fuel. The results showed that after upgrading, the volatile content of biochar ranged from 16.19% to 45.35%, and the alkali metal content, ash content, and specific surface area were significantly reduced. The optimal route for biochar production is hydrothermal carbonization–pyrolysis (P-HC), resulting in biochar with a higher calorific value, C=C structure, and increased graphitization degree. The apparent activation energy ( E ) of the sample ranges from 199.1 to 324.8 kJ/mol, with P-HC having an E of 277.8 kJ/mol, lower than that of raw biomass, primary biochar, and anthracite. This makes P-HC more suitable for blast furnace injection fuel. Additionally, the paper proposes a path for P-HC injection in blast furnaces and calculates potential environmental benefits. P-HC offers the highest potential for carbon emission reduction, capable of reducing emissions by 96.04 kg/t when replacing 40wt% coal injection.

The fuel ratio (FR) is an important parameter to characterize the carbon emission and energy consumption in the blast furnace (BF) ironmaking process. However, the BF ironmaking process is complex and there are many factors affecting the FR, which makes it difficult to predict and to quantify the impact of each parameter on the FR. In this study, different machine-learning methods were used to build predictive models of FR and to analyze the effects on it of different types of parameters. The dataset was collected over 7 years in a steelmaking factory, with 31 features. Through data cleaning, the data quality and the performance of machine-learning models were improved. It was found that Gaussian process regression (GPR) and extreme gradient boosting (XGBoost) displayed greater flexibility and accuracy in predicting the FR. The permutation feature importance (PFI) method was used to gain insight into how each parameter correlates with the target. The results indicate that random Forest regression gas utilization rate, blast volume, and oxygen enrichment are the main factors affecting the FR, and their degrees of influence are 0.73, 0.076, and 0.075, respectively.

The α-synuclein aggregates are the main component of Lewy bodies in Parkinson’s disease (PD) brain, and they showed immunotherapy could be employed to alleviate α-synuclein aggregate pathology in PD. Recently we have generated DNA aptamers that specifically recognize α-synuclein. In this study, we further investigated the in vivo effect of these aptamers on the neuropathological deficits associated with PD. For efficient delivery of the aptamers into the mouse brain, we employed modified exosomes with the neuron-specific rabies viral glycoprotein (RVG) peptide on the membrane surface. We demonstrated that the aptamers were efficiently packaged into the RVG-exosomes and delivered into neurons in vitro and in vivo. Functionally, the aptamer-loaded RVG-exosomes significantly reduced the α-synuclein preformed fibril (PFF)-induced pathological aggregates, and rescued synaptic protein loss and neuronal death. Moreover, intraperitoneal administration of these exosomes into the mice with intra-striatally injected α-synuclein PFF reduced the pathological α-synuclein aggregates and improved motor impairments. In conclusion, we demonstrated that the aptamers targeting α-synuclein aggregates could be effectively delivered into the mouse brain by the RVG-exosomes and reduce the neuropathological and behavioral deficits in the mouse PD model. This study highlights the therapeutic potential of the RVG-exosome delivery of aptamer to alleviate the brain α-synuclein pathology.

The safety and longevity of key blast furnace (BF) equipment determine the stable and low-carbon production of iron. This paper presents an analysis of the heat transfer characteristics of these components and the uneven distribution of cooling water in parallel pipes based on hydrodynamic principles, discusses the feasible methods for the improvement of BF cooling intensity, and reviews the preparation process, performance, and damage characteristics of three key equipment pieces: coolers, tuyeres, and hearth refractories. Furthermoere, to attain better control of these critical components under high-temperature working conditions, we propose the application of optimized technologies, such as BF operation and maintenance technology, self-repair technology, and full-lifecycle management technology. Finally, we propose further researches on safety assessments and predictions for key BF equipment under new operating conditions.

Iron ore powder was isothermally reduced at 1023–1373K with hydrogen/carbon monoxide gas mixture (from 0vol%H 2 /100vol%CO to 100vol%H 2 /0vol%CO). Results indicated that the whole reduction process could be divided into two parts that proceed in series. The first part represents a double-step reduction (Fe 2 O 3 →Fe 3 O 4 →FeO), in which the kinetic condition is more feasible compared with that in the second part representing a single-step reduction (FeO→Fe). The influence of hydrogen partial pressure on the reduction rate gradually increases as the reaction proceeds. The average reduction rate of hematite ore with pure hydrogen is about three and four times higher than that with pure carbon monoxide at 1173 and 1373 K, respectively. In addition, the logarithm of the average rate is linear to the composition of the gas mixture. Hydrogen can prominently promote carbon deposition to about 30% at 1023 K. The apparent activation energy of the reduction stage increases from about 35.0 to 45.4 kJ/mol with the increase in hydrogen content from 20vol% to 100vol%. This finding reveals that the possible rate-controlling step at this stage is the combined gas diffusion and interfacial chemical reaction.

Viscosity is considered to be a significant indicator of the metallurgical property of blast furnace (BF) slag. However, a BF is a complicated black box so that the measurement of the viscosity has a large hysteresis. A prediction model for the viscosity based on machine learning, principal component analysis (PCA) and k-nearest neighbor (KNN) regression is presented in this article. First, the main influencing factors of the viscosity are analyzed and selected as the input of the model. Then, the two datasets are preprocessed by data normalization. In addition, the sample characteristics of the data are processed to be statistically irrelevant by PCA. Based on the above, the two datasets are applied to the PCA–KNN model and the support vector regression model, respectively. The results show that the predicted result using the PCA–KNN model is more accurate and reaching 99%.

Photovoltaic (PV) microgrids comprise a multitude of small PV power stations distributed across a specific geographical area in a decentralized manner. Computational services for forecasting the output power of power stations are crucial for optimizing resource deployment. This paper proposes a deep-learning-based architecture for short-term prediction of PV power. Firstly, in order to make full use of the spatial information between different power stations, a spatio–temporal feature fusion method is proposed. This method is capable of exploiting both the power information of neighboring power stations with strong correlations and meteorological information with the PV feature data of the target power station. By using a multiscale convolutional neural network–long short-term memory (CNN-LSTM) network model, it is capable of generating a PV feature dataset containing spatio–temporal attributes that expand the data source and enhance the feature constraints. It is capable of predicting the output power sequences of power stations in PV microgrids with high model generalization and responsiveness. To validate the effectiveness of the proposed framework, an extensive numerical analysis is also conducted based on a real-world PV dataset.

Background Neurodegeneration is a chronic, progressive process initiated by early neurite retraction, a pathological hallmark preceding neuronal death in neurodegenerative disorders such as Parkinson’s disease (PD). Cleavage of gasdermin E (GSDME) releases its N-terminal domain (GSDME-N), which has been shown to mediate mitochondrial dysfunction and the onset of neurodegeneration. However, the therapeutic potential of targeting GSDME-N for early intervention in PD, and whether nicotine, a tobacco component with neuroprotective properties, acts through GSDME modulation remain unexplored. Methods The SH-SY5Y cells, primary neurons and C57BL mice were treated with rotenone to model neurodegeneration in PD. Mitochondrial accumulation of GSDME-N and activation of autophagy were assessed via immunoblotting and immunostaining. Mitochondrial membrane potential was evaluated using JC-1 and TMRM fluorescent dyes, while mitochondrial reactive oxygen species were detected with MitoSOX™ Red superoxide indicator. For morphological analysis, immunofluorescence staining was performed using antibodies against microtubule-associated protein 2 (MAP2) to visualize neurites, along with MitoTracker to label mitochondria within neurites. In vivo, C57BL/6 mice were administered rotenone and provided with nicotine supplementation in their drinking water. Motor function was assessed using rotarod, hanging wire and pole tests. Tyrosine hydroxylase (TH)-positive neurons and fibers were detected using immunohistochemistry. Results We demonstrated that GSDME-N accumulation on mitochondria during early neurodegeneration was suppressed by nicotine, thereby maintaining mitochondrial homeostasis and preventing neurite retraction. Mechanistically, nicotine activated autophagic pathway to promote GSDME-N clearance, which maintained mitochondrial membrane potential, reduced reactive oxygen species (ROS) production, and specifically rescued mitochondrial function. This protective mechanism significantly attenuated early pathological changes, including neurite retraction and loss. In a rotenone-induced PD model, nicotine treatment effectively reduced GSDME-N accumulation and decelerated neurodegenerative progression. Conclusions These findings reveal the critical role of GSDME-N in early-stage mitochondrial damage in PD and propose a novel therapeutic strategy targeting the nicotine-autophagy axis to counteract GSDME-N-mediated neurodegeneration.

Aptamers are small single-stranded DNA or RNA oligonucleotide fragments or small peptides, which can bind to targets by high affinity and specificity. Because aptamers are specific, non-immunogenic and non-toxic, they are ideal materials for clinical applications. Neurodegenerative disorders are ravaging the lives of patients. Even though the mechanism of these diseases is still elusive, they are mainly characterized by the accumulation of misfolded proteins in the central nervous system. So it is essential to develop potential measures to slow down or prevent the onset of these diseases. With the advancements of the technologies, aptamers have opened up new areas in this research field. Aptamers could bind with these related target proteins to interrupt their accumulation, subsequently blocking or preventing the process of neurodegenerative diseases. This review presents recent advances in the aptamer generation and its merits and limitations, with emphasis on its applications in neurodegenerative diseases including Alzheimer’s disease, Parkinson’s disease, transmissible spongiform encephalopathy, Huntington’s disease and multiple sclerosis.