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IntroductionMandible distraction osteogenesis (MDO) is widely used to reconstruct the mandible in patients with mild Hemifacial microsomia (HFM). However, the masseter's response to mandible distraction remains unclear.MethodsIn this study, we analyze the effect of the surgical intervention on masseter muscle by a retrospective analysis. The procedure consisted of a five-day latent period, a three-week distraction period, and a six-month consolidation period. CT data were manually segmented and measured with Mimics software before surgery, within 3 months, and more than 1 year postoperatively. Masseter volume, masseter length, masseter width, and mandible ramus height were measured and analyzed using paired t-test, Pearson, and Spearman correlation analysis.ResultsWe included 21 patients with HFM who underwent mandible distraction osteogenesis from 2015 to 2020. The masseter volume on the affected side increased immediately after surgery from (6,505.33 ± 3,671.95) mm3 to (10,194.60 ± 5638.79) mm3, but decreased to (8,148.38 ± 3,472.57) mm3 at the second follow-up correlated to mandible ramus height (r = 0.395, P = 0.038). A similar trend was observed in changes in masseter length. Symmetry and width of masseter muscle had no longitudinal statistical significance.DiscussionMasseter muscle involvement benefits from MDO in the short term. To achieve long-term efficacy, more attention should be paid to muscle reconstruction.

Abstract This study aimed to probe into the anatomic course of inferior alveolar nerve canal (IANC) in hemifacial microsomia (HFM) on a large scale, morphological observations and further quantitative study were performed. Patients were classified by Pruzansky–Kaban classification. The anatomic course of IANC was analyzed morphologically with three-dimensional (3D) imaging software among 248 patients. Seven distances between fixed landmarks on both sides were measured for 236 patients. The differences between affected and unaffected sides were compared. Significant differences were found in the entrance (P

Abstract Background Mandibular distraction osteogenesis (MDO) is a major part of the treatment for hemifacial microsomia patients. Due to the narrow surgical field of the intraoral approach, osteotomy accuracy is highly dependent on the surgeons’ experience. Electromagnetic (EM) tracking systems can achieve satisfying accuracy to provide helpful real-time surgical navigation. Our research team developed an EM navigation system based on artificial intelligence, which has been justified in improving the accuracy of osteotomy in the MDO in animal experiments. This study aims to clarify the effect of the EM navigation system in improving the MDO accuracy for hemifacial microsomia patients. Methods This study is designed as a single-centered and randomized controlled trial. Altogether, 22 hemifacial microsomia patients are randomly assigned to the experiment and control groups. All patients receive three-dimensional CT scans and preoperative surgical plans. The EM navigation system will be set up for those in the experiment group, and the control group will undergo traditional surgery. The primary outcome is the surgical precision by comparing the osteotomy position of pre- and postoperative CT scan images through the Geomagic Control software. The secondary outcomes include mandibular symmetry (occlusal plane deviation angle, mandibular ramus height, and body length), pain scale, and complications. Other indications, such as the adverse events of the system and the satisfactory score from patients and their families, will be recorded. Discussion This small sample randomized controlled trial intends to explore the application of an EM navigation system in MDO for patients, which has been adopted in other surgeries such as orthognathic procedures. Because of the delicate structures of children and the narrow surgical view, accurate osteotomy and protection of nearby tissue from injury are essential for successful treatment. The EM navigation system based on artificial intelligence adopted in this trial is hypothesized to provide precise real-time navigation for surgeons and optimally improve patient outcomes, including function and aesthetic results. The results of this trial will extend the application of new navigation technology in pediatric plastic surgery. Trial registration Chinese Clinical Trial Registry ChiCTR2200061565. Registered on 29 June 2022.

Salt accumulation in bare strips under film-mulched drip irrigation is a global concern as it adversely affects soil quality and hinders sustainable agricultural development in arid and semi-arid regions. This study aims to investigate the spatial distribution of soil moisture and salt under various planting patterns and assess the lateral salt accumulation effect in bare strips. Seven treatments were implemented based on the local cotton planting pattern, including the local classical planting pattern (LTP), mulch width of 220 cm (WFM-220), spacing of 90 cm (SFM-90), mulch width of 40 cm (WFM-40), spacing of 10 cm (SFM-10), ridge tillage (TFM-RT), and ditching (TFM-D), varying in mulch width, spacing, and tillage method in bare strips. Additionally, the performance of the HYDRUS-2D model was evaluated by comparing simulated and observed values using field data. The results revealed that (I) the WFM-220 cm treatment exhibited the best water content retention under mulched film, with lower salt accumulation in the surface bare strip (0–20 cm soil layer); (II) all treatments with narrow rows showed desalination effects in the 0–40 cm soil layer, with salt content reductions ranging from approximately 13% to 38% compared to the initial values; (III) under the LTP treatment, the lateral salt discharge effect in the bare strip of the 0–40 cm soil layer was the best, regardless of mulch width and spacing, with a salt accumulation rate up to three times higher than the initial value, and even up to four times higher in the 0–10 cm layer; (IV) the TFM-RT treatment exhibited the best salt accumulation ability on the surface bare strip; and (V) the HYDRUS-2D model proved to be an effective tool for studying the dynamic regulation mechanism of water and salt with root mean square error values ranging from 0.079 to 0.106 cm3·cm−3 for soil water content and from 0.044 to 0.079 dS·m−1 for electrical conductivity, indicating good agreement between simulations and observations.

Weather-related failures significantly challenge the reliability of distribution systems. To enhance the risk management of weather-related failures, an interpretable extra-trees based weather-related risk prediction model is proposed in this study. In the proposed model, the interpretability is successfully introduced to extra-trees by analyzing and processing the paths of decision trees in extra-trees. As a result, the interpretability of the proposed model is reflected in the following three respects: it can output the importance, contribution, and threshold of weather variables at high risk. The importance of weather variables can help in developing a long-term risk prevention plan. The contribution of weather variables provides targeted operation and maintenance advice for the next prediction period. The threshold of weather variables at high risk is critical in further preventing high risks. Compared with the black-box machine learning risk prediction models, the proposed model over-comes the application limitations. In addition to generating predicted risk levels, it can also provide more guidance information for the risk management of weather-related failures.

The single-line-to-ground faults with line breaks (SLGFs-LBs) occur more and more frequently in distribution networks and can cause major safety accidents. It is difficult to distinguish the single-line-to-ground faults (SLGFs) in resonant grounding systems and ungrounding systems due to the same electrical characteristics on the source side and uncertain operation conditions of distribution networks. This paper proposes a method for distinguishing SLGFs-LBs and SLGFs. First, the source-side and load-side voltage characteristics of SLGFs and SLGFs-LBs are analyzed, and the phase difference between the voltages of the fault phase and non-fault phase on the load side is selected as the identification criterion. Phasor measurement units (PMUs) are selected as measuring devices. Then, the effects of operation conditions and external devices in distribution networks on the proposed method are discussed, and the phase errors caused by them are calculated to correct the identification method. Finally, the field testing and simulation experiments are conducted to verify the effectiveness and robustness of the proposed method.

Abstract This study aimed to investigate the accuracy and safety of mandibular osteotomy and distraction device positioning in distraction osteogenesis assisted by an electromagnetic navigation surgical robot. Twelve New Zealand white rabbits were randomly divided into two groups after computed tomography. The control group underwent a procedure based on the preoperative three-dimensional design and clinical experience. Animals in experimental group underwent a procedure with robotic assistance after registration. The accuracies of osteotomy and distraction device positioning were analysed based on distance and angular errors. The change in ramus length after a 1 cm-extension of the distraction device was for assessing distraction effect. The preparation, operative and osteotomy times, intraoperative bleeding, and teeth injury were used for safety assessment. In the experimental group, the distance (t = 2.591, p = 0.011) and angular (t = 4.276, p = 0.002) errors of osteotomy plane, and the errors in distraction device position (t = 3.222, p = 0.009) and direction (t = 4.697, p = 0.001) were lower; the distraction effect was better (t = 4.096, p = 0.002). There was no significant difference in the osteotomy time and bleeding; however, the overall operative and preparation times were increased in the experimental group, with a reduced rate of teeth damage. Robot-assisted mandibular distraction osteogenesis with electromagnetic navigation in craniofacial microsomia is feasible, safe, significantly improves surgical precision.

IntroductionThis study aimed to verify the accuracy and safety of distraction osteogenesis for hemifacial microsomia assisted by a robotic navigation system based on artificial intelligence.MethodsThe small sample early-phase single-arm clinical study, available at http://www.chictr.org.cn/index.aspx, included children aged three years and older diagnosed with unilateral hemifacial microsomia (Pruzansky-Kaban type II). A preoperative design was performed, and an intelligent robotic navigation system assisted in the intraoperative osteotomy. The primary outcome was the accuracy of distraction osteogenesis, including the positional and angular errors of the osteotomy plane and the distractor, by comparing the preoperative design plan with the actual images one week postoperatively. Perioperative indicators, pain scales, satisfaction scales, and complications at one week were also analyzed.ResultsFour cases (mean 6.5 years, 3 type IIa and 1 type IIb deformity) were included. According to the craniofacial images one week after surgery, the osteotomy plane positional error was 1.77 ± 0.12 mm, and the angular error was 8.94 ± 4.13°. The positional error of the distractor was 3.67 ± 0.23 mm, and the angular error was 8.13 ± 2.73°. Postoperative patient satisfaction was high, and no adverse events occurred.DiscussionThe robotic navigation-assisted distraction osteogenesis in hemifacial microsomia is safe, and the operational precision meets clinical requirements. Its clinical application potential is to be further explored and validated.

IntroductionThis study aims to quantitively analyze mandibular ramus and body deformities, assessing the asymmetry and progression in different components.MethodsThis is a retrospective study on hemifacial microsomia children. They were divided into mild/severe groups by Pruzansky-Kaban classification and into three age groups (

Abstract Intelligent perception is the cornerstone of digitalisation of power grid. Due to the closest connection with users, the information perception ability of distribution network directly affects the reliability of power supply. Distribution network has a wide range of geographical distribution and equipment types. The application of intelligent perception is with high complexity and dynamics under the limitation of both technology and management. Therefore, the study first sorts out the current application status of intelligent perception technology and equipment in distribution network from three application fields including distribution automation, metering systems, and inspection. Then, the constraints of existing intelligent perception applications and the key points of further research are pointed out. Finally, the development trends of intelligent perception technology in distribution network are presented from the three aspects of basic technology, perception equipment, and advanced applications. With the improvement of sensing, communication, energy acquisition, chips and perception equipments, the depth and breadth of advanced applications in distribution network will be expanded. The research results can provide references for the follow‐up research and practice of intelligent perception in distribution networks.

This study investigated the effects of illumination time and soil moisture on seed germination and seedling establishment of Magnolia sprengeri Pamp. to improve the seed germination percentage and seedling survival percentage of M. sprengeri. It is of great significance for rapid propagation, seedling regeneration, field management, and artificial high-efficiency cultivation of M. sprengeri. In this study, the seeds of natural M. sprengeri populations from original habitat were used as test materials. Seed germination and seedling establishment of M. sprengeri were performed under different illumination time and soil moisture treatments in artificial climate incubator. The study found that there were significant differences among various key parameters related to seed germination and seedling establishment under different treatments (P < 0.05). Germination percentage, germination potential, germination index, vigor index, germination rate, plant height, number of leaves, base diameter, taproot length, number of lateral roots, maximum lateral root length, single plant leaf area, fresh weight, and seedling survival rate reached the maximum at continuous illumination and 13% of soil moisture, respectively. By the integrative evaluation for the influence of these two factors on seed germination and seedling establishment, soil moisture is a dominant factor affecting seed germination and seedling establishment, whereas illumination is an important promoting factor for seedling establishment of M. sprengeri. Continuous illumination and 13% of soil moisture content is suitable for seed germination and seedling establishment of M. sprengeri.

BackgroundHead and neck squamous cell carcinoma (HNSCC), the most common head and neck cancer, is highly aggressive and heterogeneous, resulting in variable prognoses and immunotherapeutic outcomes. Natural killer (NK) cells play essential roles in malignancies’ development, diagnosis, and prognosis. The purpose of this study was to establish a reliable signature based on genes related to NK cells (NRGs), thus providing a new perspective for assessing immunotherapy response and prognosis of HNSCC patients.MethodsIn this study, NRGs were used to classify HNSCC from the TCGA-HNSCC and GEO cohorts. The genes were evaluated using univariate cox regression analysis based on the differential analysis of normal and tumor samples in TCGA-HNSCC conducted using the “limma” R package. Thereafter, we built prognostic gene signatures using LASSO-COX analysis. External validation was carried out in the GSE41613 cohort. Immunity analysis based on NRGs was performed via several methods, such as CIBERSORT, and immunotherapy response was evaluated by TIP portal website.ResultsWith the TCGA-HNSCC data, we established a nomogram based on the 17-NRGs signature and a variety of clinicopathological characteristics. The low-risk group exhibited a better effect when it came to immunotherapy.Conclusions17-NRGs signature and nomograms demonstrate excellent predictive performance and offer new perspectives for assessing pre-immune efficacy, which will facilitate future precision immuno-oncology research.

Abstract Inspection robots are popularized in substations due to the lack of personnel for operation and maintenance. However, these inspection robots remain at the level of perceptual intelligence, rather than cognition intelligence. To enable a robot to automatically detect defects of power equipment, object recognition is a critical step because criteria of infrared diagnosis vary with types of equipment. Since this task is not a big‐sample learning problem, prior knowledge needs to be added to improve existing methods. Here, an object recognition model based on human‐level concept learning is proposed, which utilizes relationship between equipment. The proposed method is composed of three parts: Mask RCNN, Bayesian Context Network and human‐level concept learning. As the backbone network, Mask RCNN, a pixel‐wise segmentation network, gives preliminary recognition results. Then, based on the object relationship graph of Bayesian Context Network, human‐level concept learning corrects the results in sequence by maximizing the conditional probability of an object given its neighbourhood. Experiments show that the accuracy of the proposed method increases 9.7% compared with its backbone network, making industrial application of this technology possible.

The relationship between oxidized lipid metabolism and the immunological function of cancer is well known. However, the functions and regulatory mechanisms of lncRNAs associated with oxidized lipid metabolism in head and neck squamous cell carcinoma (HNSCC) remain to be fully elucidated. In this study, we established an oxidized lipid metabolism-related lncRNA prognostic signature to assess the prognosis and immune infiltration of HNSCC patients. The HNSCC transcriptome was obtained from The Cancer Genome Atlas. The choice of the target genes with a relevance score greater than 10 was performed via a correlation analysis by GeneCards. Patients were categorized by risk score and generated with multivariate Cox regression, which was then validated and evaluated using the Kaplan–Meier analysis and time-dependent receiver operating characteristics (ROC). A nomogram was constructed by combining the risk score with the clinical data. We constructed a risk score with 24 oxidized lipid metabolism-related lncRNAs. The areas’ 1-, 2-, and 3-year OS under the ROC curve (AUC) were 0.765, 0.724, and 0.724, respectively. Furthermore, the nomogram clearly distinguished the survival probabilities of patients in high- and low-risk groups, between which substantial variations were revealed by immune infiltration analysis. The results supported the fact that oxidized lipid metabolism-related lncRNAs might predict prognoses and assist with differentiating amid differences in immune infiltration in HNSCC.

In distribution networks with arc suppression coil grounding, the fault line can be set as the energy leakage channel of the capacitive current of the entire system. During the period from fault occurrence to the whole action process of arc suppression coil, there is abundant fault information in the transient waves. However, the data of this period are usually ignored in previous methods. In this study, a novel fault section location method based on the leakage energy function is proposed. Only the fault phase voltage and fault phase current are used in the proposed method, to avoid the difficulty of the zero‐sequence current measurement. Then, the action characteristics of two types of arc suppression devices are analysed. The influence of the capacitor current on the fault phase voltage and current during the entire process of arc suppression coil operation is deduced. The difference between the leakage energy on both sides of the fault section is used as the criterion for fault section location. Finally, the effectiveness of the method is verified by a case study. The simulation results indicate that the proposed method has high sensitivity and high impedance adaptability, that will have more advantages in the actual situations.

Warming can affect soil microbial respiration by changing microbial biomass and community composition. The responses of soil microbial respiration to warming under experimental conditions are also related to background conditions and the experimental setup, such as warming magnitude, duration, and methods. However, the global pattern of soil microbial respiration in response to warming and underlying mechanisms remain unclear. Here, we conducted a global meta-analysis of the response of soil microbial respiration to warming by synthesizing data from 187 field experiments. We found that experimental warming significantly increased soil microbial respiration and microbial biomass carbon by 11.8% and 6.4%, respectively. The warming-induced increase in microbial carbon decomposition was positively correlated with increased microbial biomass carbon, but not community composition. Moreover, the positive response of soil microbial respiration marginally increased with warming magnitude, particularly in short-term experiments, but soil microbial respiration adapted to higher warming at longer timescales. Warming method did not significantly affect the response of microbial respiration, except for a significant effect with open top chamber warming. In addition, the impact of warming on soil microbial respiration was more pronounced in wetter sites and in sites with lower soil pH and higher soil organic carbon. Our findings suggest that warming stimulates microbial respiration mainly by increasing microbial biomass carbon. We also highlight the importance of the combination of warming magnitude and duration in regulating soil microbial respiration responses, and the dependence of warming effects upon background precipitation and soil conditions. These findings can advance our understanding of soil carbon losses and carbon-climate feedbacks in a warm world.

Fe-Ni-Cr austenitic alloys are widely used in hydrogen environments as structural materials. Their weld normally shows higher hydrogen-embrittlement sensitivity than the base metal, endangering large-scale applications. Herein, by using electron microscopy and numerical calculations, the influence of applied load on the fracture mode of hydrogen-embrittled JBK-75 alloy weldment is revealed and correlated with a competition between hydrogen-prompted intergranular decohesion (HPID) and hydrogen-enhanced localized plasticity (HELP). Therefore, independent of the load levels, the weld featuring a lower strength and smaller grain sizes is always more vulnerable to hydrogen embrittlement than the base metal.

ABSTRACTMethicillin-resistant Staphylococcus aureus (MRSA), also known as oxacillin-resistant S. aureus, is a leading cause of community and hospital associated infections globally. In this work, we found that deletion of the arlRS two-component system genes in the USA300 and USA500 strains resulted in increased susceptibilities to oxacillin (8–16-fold decrease in minimal inhibitory concentrations). In USA300ΔarlRS, transcriptional levels of mecA or blaZ showed no obvious change, while mRNA levels of spx showed a 4-fold decrease at 4 h and a 6.3-fold decrease at 10 h. Overexpression of spx in ΔarlRS restored oxacillin resistance to a similar level in USA300. In addition, gel shift assay showed that the recombinant ArlR bound to spx promoter region. Furthermore, silencing of spx led to a significant increase of oxacillin susceptibility in multiple MRSA isolates. Our results indicate that ArlRS plays a strong role in regulating oxacillin resistance in MRSA strains, which involves direct modulation of spx expression. Moreover, oritavancin showed inhibition to ATPase activity of the recombinant histidine kinase ArlS (IC50 = 5.47 μM). Oritavancin had synergy effect on oxacillin activity against the MRSA strains in both planktonic and biofilm state. Our data suggest that ArlRS is an attractive target for breaking antimicrobial resistance of MRSA.

This paper explores a novel idea for power equipment monitoring and finds that random matrix theory is suitable for modeling the massive data sets in this situation. Big data analytics are mined from those data. We extract the statistical correlation between key states and those parameters. In particular, the (empirical) eigenvalue spectrum distribution and the (theoretical) single ring law are derived from large-dimensional random matrices whose entries are modeled as time series. The radii of the single ring law are used as statistical analytics to characterize the measured data. The evaluation of key state and anomaly detection are accomplished through the comparison of those statistical analytics.

In response to the rapid growth of credit-investigation data, data redundancy among credit-investigation agencies, privacy leakages of credit-investigation data subjects, and data security risks have been reported. This study proposes a privacy-protection scheme for a credit-investigation system based on blockchain technology, which realizes the secure sharing of credit-investigation data among multiple entities such as credit-investigation users, credit-investigation agencies, and cloud service providers. This scheme is based on blockchain technology to solve the problem of islanding of credit-investigation data and is based on zero-knowledge-proof technology, which works by submitting a proof to the smart contract to achieve anonymous identity authentication, ensuring that the identity privacy of credit-investigation users is not disclosed; this scheme is also based on searchable-symmetric-encryption technology to realize the retrieval of the ciphertext of the credit-investigation data. A security analysis showed that this scheme guarantees the confidentiality, the availability, the tamper-proofability, and the ciphertext searchability of credit-investigation data, as well as the fairness and anonymity of identity authentication in the credit-investigation data query. An efficiency analysis showed that, compared with similar identity-authentication schemes, the proof key of this scheme is smaller, and the verification time is shorter. Compared with similar ciphertext-retrieval schemes, the time for this scheme to generate indexes and trapdoors and return search results is significantly shorter.

Oxide dispersion strengthened ferritic steel is considered an important structural material in fusion reactors due to its excellent resistance to radiation and oxidation. Fine and dispersed oxides can be introduced into the matrix via the powder metallurgy process. In the present study, large grain sizes and prior particle boundaries (PPBs) formed in the FeCrAlY alloy prepared via powder metallurgy. Thermo-mechanical treatment was conducted on the FeCrAlY alloy. Results showed that microstructure was optimized: the average grain diameter decreased, the PPBs disappeared, and the distribution of oxides dispersed. Both ultimate tensile strength and elongation improved, especially the average elongation increased from 0.5% to 23%.

The high tensile strength and irradiation resistance of oxide dispersion strengthened (ODS) ferritic steels is attributed to the ultrafine and dispersed oxides within the matrix. The high content of oxygen and yttrium is critical for the formation of dense Y-rich oxides. However, only few studies have reported the effect of oxygen content on the microstructure and mechanical properties of ODS steels. Herein, we employed gas atomization reactive synthesis to prepare pre-alloy powders and then hot isostatic pressing (HIP) to consolidate two 22Cr-5Al ODS steels with different oxygen content. Our results showed Y-rich precipitates at and near grain boundaries of the as-HIPed alloys. Moreover, with the oxygen content increasing from 0.04 to 0.16 wt%, more precipitates precipitated in the as-HIPed alloy, and the ultimate tensile strength of the alloy was improved. However, increasing the oxygen content to 0.16 wt% led to formation of stripe and chain precipitates at and near grain boundaries, which caused a partial intergranular fracture of the as-HIPed alloy.

The demand for heat-resistant steel has increased owing to its utility in numerous devices that must withstand high steam pressures and high temperatures, such as turbine rotors and blades in ultra-supercritical power plants. It is inevitable to join heat-resistance steel part by welding method, so it is important to maintain the toughness of the weld metals. In this study, the microstructure, low-temperature impact toughness, and fracture surface of as-welded and post-weld heat treatment (PWHT) of 2.25Cr-1Mo-0.25V weld metal were investigated. The microstructures of the as-welded and PWHT specimens are granular bainite and ferrite, respectively. This work revealed the relationship between effective microstructure nearby crack initiation origin and low temperature impact toughness for both the as-welded and PWHT specimens. The evolution of the microstructure and prior austenite was then investigated using confocal laser scanning microscopy (CLSM) to observe the formation of coarse ferrite grain structures. A suggestion for enhancing the low-temperature toughness was provided based on the effect of adjusting Mn content and forming acicular ferrite.

It has been previously shown that inhaled zinc oxide nanoparticles (ZnO-NPs) can modulate inflammation. MicroRNAs (miRNAs) enclosed in exosomes have been identified as an important signature for inflammatory responses. However, the role of exosomal miRNAs during pathogenic inflammation has not been investigated. Healthy rats were exposed to ZnO-NPs (41.7 nm; 2, 4, and 8 mg/kg) or saline (control) via oropharyngeal aspiration. ZnO-NPs induced significant increases in the serum levels of interleukin 8 (IL-8), interleukin-1 beta (IL-1β), and tumor necrosis factor α (TNF-α), and elevated the number of cells and the percentage of neutrophils in the blood. Moreover, exposure to ZnO-NPs increased the levels of lactate dehydrogenase (LDH) activity and total protein in bronchoalveolar lavage fluid (BALF). Differential profiling of miRNAs in isolated serum exosomes revealed that 16 miRNAs were up-regulated and 7 down-regulated in ZnO-NP-treated rats compared with the controls. Functional and pathway analysis indicated that miRNAs may participate in inflammation directly and indirectly through protein and vesicle-mediated transport or regulation of IL-1, oxidative stress, apoptosis, and autophagy. These results suggest that miRNAs in serum exosomes are involved in pulmonary neutrophilic inflammation induced by ZnO-NPs.

Cold metal transfer process is applied to join titanium and Q235 steel with copper filler metal. Scanning electron microscope (SEM), energy dispersive spectrometer (EDS) analysis, micro-hardness tests, and tensile strength test were performed to investigate the joining mechanism and strength of joints. The results show that the stacking order of two base metals affected the joining modes and strength. For top Q235 steel to bottom Ti-TA2 lapped joint, there was no distinct interface reaction layer between the steel base metal and the weld metal; dispersed TiFe2 intermetalics (IMCs) IMCs between the steel base metal and the Ti base metal greatly improved the strength of joint; the tensile force of the joint could reach up to 93% that of steel-steel joint using the same welding parameters. Additionally, the joints were fractured in dimple mode at the steel base metal. For top Ti-TA2 to bottom Q235 steel lapped joint, the increasing volume fraction of Ti-Cu IMCs at the Ti-Cu weld metal interface contributed to the strength of joint degradation. The joints under tensile loading are initiated at the Ti-Cu weld metal interface between the weld metal and Ti base metal, then propagated to weld metal, finally fractured with brittle mode.

The growth of individual trees in a forest is affected by many factors, a crucial one being the intensity of competition among trees, because it affects the spatial structure of the forest and is in turn influenced by silvicultural practices. In a mixed forest in particular, the growth of trees is affected by multiple interactions. To analyse the competition between moso bamboo (Phyllostachys pubescens (Pradelle) Mazel ex J.Houz.) and broad-leaved trees in a mixed forest, data were extracted by sampling six spots within such a forest using terrestrial laser scanning (TLS). The convex hull algorithm was used for calculating the overlap volume between the crowns of the broad-leaved trees and the bamboo canopy. Bamboos growing at least 3 m away from any of the broad-leaved trees were the most numerous and the diameter at breast height (DBH) is larger than those growing closer than that, which suggests that broad-leaved trees suppressed the growth of bamboo if they are closer but promote it beyond 3 m up to a point at which the distance is too great for any such effect. The modified Hegyi’s competition index was constructed based on the canopy factor, which may better describe the competitive interaction among the trees and bamboos. Using TLS can enhance our understanding of the competition among trees in mixed forests and help in planning the spatial structure of such forests in general and provide a benchmark for choosing planting distances in particular.

It is of great significance to accurately get the running state of power transformers and timely detect the existence of potential transformer faults. This paper presents a prediction method of transformer running state based on LSTM_DBN network. Firstly, based on the trend of gas concentration in transformer oil, a long short-term memory (LSTM) model is established to predict the future characteristic gas concentration. Then, the accuracy and influencing factors of the LSTM model are analyzed with examples. The deep belief network (DBN) model is used to establish the transformer operation using the information in the transformer fault case library. The accuracy of state classification is higher than the support vector machine (SVM) and back-propagation neural network (BPNN). Finally, combined with the actual transformer data collected from the State Grid Corporation of China, the LSTM_DBN model is used to predict the transformer state. The results show that the method has higher prediction accuracy and can analyze potential faults.

The purpose of analyzing the dissolved gas in transformer oil is to determine the transformer’s operating status and is an important basis for fault diagnosis. Accurate prediction of the concentration of dissolved gas in oil can provide an important reference for the evaluation of the state of the transformer. A combined predicting model is proposed based on kernel principal component analysis (KPCA) and a generalized regression neural network (GRNN) using an improved fruit fly optimization algorithm (FFOA) to select the smooth factor. Firstly, based on the idea of using the dissolved gas ratio of oil to diagnose the transformer fault, gas concentration ratios are also used as characteristic parameters. Secondly, the main parameters are selected from the feature parameters using the KPCA method, and the GRNN is then used to predict the gas concentration in the transformer oil. In the training process of the network, the FFOA is used to select the smooth factor of the neural network. Through a concrete example, it is shown that the method proposed in this paper has better data fitting ability and more accurate prediction ability compared with the support vector machine (SVM) and gray model (GM) methods.