Abnormal Drop Research Articles

Septal Venous Channel Perforation during Left Bundle Branch Area Pacing: A Prospective Study.

To characterize the diagnosis, frequency, and procedural implications of septal venous channel perforation during left bundle branch area pacing (LBBAP). All consecutive patients undergoing LBBAP over an 8-month period were prospectively studied. During lead placement, obligatory septal contrast injection was performed twice, at initiation (implant entry zone) and completion (fixation zone). An intuitive fluoroscopic schema using orthogonal views (LAO/RAO) and familiar landmarks is described. Using this, we resolved zonal distribution (I-VI) of lead position on the ventricular septum and its angulation (post-fixation angle θ). Subjects with/without septal venous channel perforation were compared. Sixty-one-patients [Male 57.3%, Median Age (IQR) 69.5(62.5-74.5) years] were enrolled. Septal venous channel perforation was observed in 8 (13.1%) patients [Male 28.5%, Median Age (IQR) 64(50-75) years]. They had higher frequency of, i) right-sided-implant (25% vs. 1.9%, p=0.04), ii) fixation in zone III at the mid-superior septum (75% vs 28.3%, p=0.04), iii) steeper angle of fixation- median θ (IQR) [19(10-30)° vs. 5(4-19)°, p=0.01), and iv) longer median penetrated-lead-length (IQR) [13(10-14.8) vs. 10(8.5-12.5)mm, p=0.03]. Coronary sinus drainage of contrast was noted in 5 (62.5%) patients. Abnormal impedance drops during implantation (12.5% vs. 5.7%, p=NS) were not significantly different. When evaluated systematically, septal venous channel perforation may be encountered commonly after LBBAP. The fiducial reference framework described using fluoroscopic imaging identified salient associated findings. This may be addressed with lead repositioning to a more inferior location and are not associated with adverse consequence acutely or in early follow-up.

Study on the machine-learning based system for detecting abnormal pressure drops in hydraulic press machines

Study on the machine-learning based system for detecting abnormal pressure drops in hydraulic press machines

An efficient segmentation model for abnormal chicken droppings recognition based on improved deep dual-resolution network.

The characteristics of chicken droppings are closely linked to their health status. In prior studies, chicken droppings recognition is treated as an object detection task, leading to challenges in labeling and missed detection due to the diverse shapes, overlapping boundaries, and dense distribution of chicken droppings. Additionally, the use of intelligent monitoring equipment equipped with edge devices in farms can significantly reduce manual labor. However, the limited computational power of edge devices presents challenges in deploying real-time segmentation algorithms for field applications. Therefore, this study redefines the task as a segmentation task, with the main objective being the development of a lightweight segmentation model for the automated monitoring of abnormal chicken droppings. A total of 60 Arbor Acres broilers were housed in 5 specific pathogen-free cages for over 3 weeks, and 1650 RGB images of chicken droppings were randomly divided into training and testing sets in an 8:2 ratio to develop and test the model. Firstly, by incorporating the attention mechanism, multi-loss function and auxiliary segmentation head, the segmentation accuracy of the DDRNet was enhanced. Then, by employing the group convolution and an advanced knowledge distillation algorithm, a lightweight segmentation model named DDRNet-s-KD was obtained, which achieved a mean Dice coefficient (mDice) of 79.43% and an inference speed of 86.10 frames per second (FPS), showing a 2.91% and 61.2% increase in mDice and FPS compared to the benchmark model. Furthermore, the DDRNet-s-KD model was quantized from 32-bit floating-point values to 8-bit integers and then converted to TensorRT format. Impressively, the weight size of the quantized model was only 13.7 MB, representing an 82.96% reduction compared to the benchmark model. This makes it well-suited for deployment on the edge device, achieving an inference speed of 137.51 FPS on Jetson Xavier NX. In conclusion, the methods proposed in this study show significant potential in monitoring abnormal chicken droppings and can provide an effective reference for the implementation of other agricultural embedded systems.

Different spectral features near the energy bandgaps of normal and inverse heterostructures of In<sub>0.52</sub>Al<sub>0.48</sub>As/InP

Photoconductivity (PC) spectroscopy and photoluminescence (PL) spectroscopy were used to characterize two heterostructure configurations of InAlAs/InP grown by molecular beam epitaxy (MBE) on the InP (100) substrate. The sample A is the type called normal heterostructure, which has an In<sub>0.52</sub>Al<sub>0.48</sub>As layer grown on InP, while sample B is called the inverse type formed by an InP cap layer on In<sub>0.52</sub>Al<sub>0.48</sub>As. The front excitation was employed in both PC experiment and PL experiment and the measurements were conducted at 77 K. The PC spectrum of sample A shows an abnormal step-like drop when the photon energy is larger than the energy band gap of In<sub>0.52</sub>Al<sub>0.48</sub>As. The phenomenon implies that the conductance of sample is a multilayer effect including the contribution of interfacial two-dimensional electron gas (2DEG). Moreover, a conductance peak is observed at 916 nm below the bandgap of InP. Accordingly, an intense luminescent peak at the wavelength manifests in the PL spectrum. The origin of the 916 nm peak is attributed to the recombination of 2DEG electrons with the valence band holes excited near the interface. However, the spectral feature of the above energy does not exist in both PC and PL spectra of sample B. This difference may be explained by the different interface electronic structures of the inverse interface. For the latter case, considering that a graded variation in In-As-P composition is related to the inverse interface of InP/InAlAs, the band bending effect should be weak. In such a case, the bound energy of 2DEG in the interface potential well is raised closer to the conductance band of the bulk. Consequently, the recombination energy of 2DEG at the inverse interface with the holes in the valence band is close to the band-to-band transition of InP bulk and the luminescence is difficult to be distinguished from that of bulk InP. The work also demonstrates that the comparative study with both PC technique and PL technique is helpful to provide a full insight into the interface electronic property.

Effect of hydrodynamic aeration on bioelectricity generation from photoautotrophic Spirulina platensis in biophotovoltaic devices

A biophotovoltaic (BPV) device is a type of bioelectrochemical cell that shows potential for renewable energy production using photosynthetic exoelectrogenic microorganisms. However, the technology currently has limitations due to its relatively low power output and high dependence on biofilm attachment. Thus, this paper examines the effect of hydrodynamic aeration on the bioelectricity generation of BPV devices. Aeration is widely applied in microalgae cultivation to improve the mixing effect between microalgae cells, culture medium, and air bubbles. The methodology involves the cultivation of Spirulina platensis, device fabrication, voltage measurement in both non-aeration and aeration modes, and the use of polarization curves to determine the device performance under a gradually rising external load. The BPV devices are affected by abnormal voltage drop under high external loads, which is called power overshoot. The use of aeration successfully solved the power overshoot issues over time, but it also resulted in a gradual reduction in the voltage output and power density of the BPV devices. During aeration, the BPV devices produced a peak power density of 0.121 ± 0.056 mWm−2 on Day 1, which declined to 0.048 ± 0.025 mWm−2 on Day 13. The blowing of air bubbles had some side effects on BPV devices, including causing the attachment of Spirulina cells to the container walls and accelerating the evaporation of the culture medium. This study emphasizes the role of hydrodynamic aeration in the bioelectricity generation process and opens up possibilities for wider application of BPV devices in renewable energy production.

In family we trust—In good and bad times

AbstractThis short report investigates the stock market behavior of Swiss companies during the COVID‐19 pandemic. Results suggest that family firms performed better during the outbreak and post‐lockdown periods than widely‐held firms. Family firms also displayed a larger abnormal trading volume drop than widely‐held companies. In size‐sorted subsamples, the volume difference appears more pronounced for smaller firms. We explain these findings by family firms, especially smaller ones, predominantly attracting investors with a long‐term horizon. Such investors are less likely to sell during market turmoil, making family firms not only less liquid but also less sensitive to market fluctuations.

The effect of oregano essential oil on Feed Passage Syndrome in broilers: 1. Assessment under field conditions

Feed Passage Syndrome (FPS) is a prevalent condition that includes wet excreta, with undigested feed, and loss of feed efficiency, with multiple predisposing factors involved. Alternatives to antimicrobials, including phytogenics, are being studied to control this process. This study aimed to assess oregano essential oil (OEO) in broilers subjected to a natural field FPS challenge. Four broiler houses (two males; two females) presenting FPS were divided into two groups per house and assigned to a control or OEO-treated group through drinking water for five days. The FPS was characterized by >30% wet droppings, >50% with undigested feed, <5% with desquamated mucosa, >10% birds with enteritis, and 90% with undigested feed in the lower ileum, and flocks clinically positive for dysbacteriosis. After a five-day treatment, OEO-treated birds showed fewer abnormal droppings (P < 0.05), reduced the prevalence of dysbacteriosis (P = 0.0404), and a progressively recovering BW. The OEO treatment showed to influence the Gompertz growth curve parameters (growth rate, P = 0.0165; inflection point, P = 0.0359), potentially reducing the age at the target market weight in 0.8 days. In conclusion, the results indicate that the OEO treatment in drinking water for broilers naturally challenged with the Feed Passage Syndrome may improve excreta characteristics and attenuate the negative impact of the syndrome on the BW.

Study on anode single-phase flow pressure drop law of proton exchange membrane fuel cell

This article briefly describes the research status of water flooding and pressure drop (PD) of the fuel cell. Then, a mathematical model of the single-phase flow PD of the fuel cell anode is established using hydrodynamics theory and referring to existing research. Then, the three-dimensional fuel cell model is established with Gambit software, and the grid is divided. Then, the numerical simulation of the single-phase flow of the fuel cell is carried out with Fluent software. Through the analysis of the simulation data, the relationship between the fuel cell anode pressure drops and some main influencing factors under steady-state conditions is explored, and the fuel cell anode’s single-phase flow pressure drop model is preliminarily verified. Anodes are highly sensitive to water flooding. The study of the single-phase flow PD law has a good contrast effect on the abnormal pressure drop during water flooding of fuel cells, which can be used to diagnose water flooding.

New Ottoman Tombstones and Inscriptions from Izmail Fortress

In July-September 2022, during an abnormal drop in the water level of the Danube, near Izmail fortress at the bottom of the river, on a plot of 150-170×10-15 m at a depth of 1 to 1.5 m, local historians accidentally discovered three clusters of artifacts: fragments of marble tombstones, fragments of Ottoman ceramics, forged iron elements of rigging, as well as the remains of the hull of a small wooden sailboat and other finds (intact and fragmented cast iron cannonballs, bombs, and a flintlock pistol), which were more or less evenly scattered over the entire designated area.&#x0D; The purpose of the research paper is to publish and analyze the most interesting and unique complex of artifacts found at the bottom of the river, namely intact and fragmented marble tombstones, both with and without epitaphs.&#x0D; Scientific novelty. In the course of the research, conducted by the expedition of the Institute of Archeology of the National Academy of Sciences of Ukraine, it has become possible to expand significantly historical and archaeological knowledge about the history of the Izmail fortress during the late Ottoman period of its existence, mainly in the 18th – early 19th century.&#x0D; Conclusions. Thanks to a rare natural phenomenon and the attentiveness of local historians, the collection of Ottoman tombstones from Izmail is augmented with 10 marble objects all at the same time, on which there are four epitaphs. Although only one of them is dated 1749, which, unfortunately, does not extend the chronology of the known epitaphs of 1719-1756, however, the objects contain other, previously unknown historical information. On two slabs, former military men of the Ottoman Porte are mentioned – the shahid and officer Ismail Aga and the nameless janissary of the 12th Orta. From the inscription on the first of them, we learned about Ismail Aga’s father – Emrullah Çelebi, who could have been a resident of Izmail and a teacher at a local educational institution. We should also mention the slab with the names of God, which could have been the amulet of the only preserved mosque in Izmail.

Speculation and clarification announcements on stock price fluctuations: Why are rumours plausible and hard to clarify?

During the COVID-19 pandemic, stock markets were fragile and sensitive to downside news regardless of whether the news was true. In China, stock rumours are increasingly rampant, affecting the sound development of the capital market. By manually gathering a sample of rumours about Chinese A-share firms, this paper studies the effects of stock market rumours and the corresponding rumour clarifications on stock returns. The study suggests that rumours rely on the information environment to persuade the market through the media effect. In terms of information disclosure, for firms that previously disclosed “negative news”, stock prices would experience abnormal drops when negative rumours appear. In terms of the media effect, rumours released by leading media cause even more significant abnormal fluctuations in stock prices. Further study shows that positive rumours significantly cause an abnormal rise in state-owned enterprises' stock prices, while negative rumours significantly cause an abnormal decline in small and medium enterprise board (SME) and growth enterprise market board (GEM) stock prices. From the perspective of the effect of clarification announcements in restraining stock price fluctuations, clear and timely clarifications are recommended.

Detection of abnormal chicken droppings based on improved Faster R-CNN

The characteristics of chicken droppings are closely related to the health of chickens. Veterinarians often judge the health of a chicken by looking at whether the chicken poop is normal. At present, the inspection of abnormal chicken droppings in chicken coops relies on manual observation, which is inefficient, accurate varies from person to person, labor-intensive, and has the risk of cross-infection. To achieve efficient, accurate, and intelligent identification of abnormal chicken droppings, an abnormal chicken droppings detection method based on improved Faster Region-based Convolutional Neural Network (Faster R-CNN) was proposed in this study. In the feature extraction network stage, deformable convolution was used and combined with Path Augmentation-Feature Pyramid Network (PA-FPN) to improve the extraction ability of features at different scales. In the Region Proposal Network (RPN) stage, the K-means++ algorithm was used to cluster the dataset and obtain the Anchor-ratio which is more suitable for the chicken poop object, and the FocalLoss classification loss function was used to improve the classification ability of difficult samples. In the regional convolutional network stage, the region of interest calibration algorithm was used instead to obtain more accurate localization information. The experimental results show that the improved Faster R-CNN structure can reach an accuracy of 98.8% for abnormal chicken poop detection, and the average accuracy mean value was improved by 27.8%. The results can provide a key core technology support for establishing an efficient abnormal chicken droppings online detection system. Keywords: abnormal chicken droppings, Faster R-CNN, detection, non-destructive monitoring, PA-FPN DOI: 10.25165/j.ijabe.20231601.7732 Citation: Zhu J H, Cui Z H, Wang H Y, Zhou M, Sun X Q. Detection of abnormal chicken droppings based on improved Faster R-CNN. Int J Agric & Biol Eng, 2023; 16(1): 243–249.

A realistic model of temperature dependent carrier diffusion constant in MAPbI3 films

The charge carrier mobility in perovskites is known to be affected by temperature. However, at low temperatures the perovskite film bears interfacial strains by deformation between the film and its substrate with difference coefficients of thermal expansion. Herein we provide a more realistic temperature-dependent carrier dynamics model involving both interfacial strain effect and intrinsic optoelectronic properties. The interfacial strain of a MAPbI3 film is confirmed through in-situ temperature-dependent X-ray diffractions. The carrier dynamics at different temperatures are unraveled using carrier dynamics fittings to time-resolved photoluminescence decays. An abnormal drop in carrier mobility is revealed with continuously decreasing temperature. The strain affects the trap density in films thus declines the carrier mobility at low temperatures. The carrier losses at the buried surface become significant at low temperatures due to the interfacial strain. This work provides a reminder that substrates should be carefully considered in temperature-related applications of perovskites.

QUANTITATIVE RISK ASSESSMENT OF THE EFFECT OF SAND ON MULTIPHASE FLOW IN PIPELINE

The presence of sand particles flowing along with reservoir fluids in a pipeline increases the probability of pipeline failure. The risk of pipeline failure is either accentuated or abated by the flow conditions of the fluids in the pipeline. In this study, a quantitative risk analysis of the effect of sand on pipelines during multiphase flow, under the pipeline failure modes; sanding up, erosion, and encountering abnormal pressure gradient was conducted. Three piping components were considered: line pipe (nominal size 1.5 in [3.8 cm]), swing check valve (nominal size 12.007 in [30.5cm]) and 90 deg LR Elbow (nominal size 2.25 in [5.7cm]). Correlations that indicate the critical velocities and the critical sand concentrations above/below which these failures occur were employed and implemented in a Visual Basic program. The analysis was conducted at a temperature of 204 °C and pressure of 604 psi [4.2×106 Pa]. A probability distribution, simulating real-life scenario was developed using Monte Carlo simulation. This determines the probability of deriving critical sand concentration values that fall beyond the set statistical limits which indicates the probability of occurrence of the failure being investigated. For all three failures, the severity of occurrence (represented by CAPEX incurred in solving the failures) was multiplied with the probability of failure which gave rise to the risk indexes. Based on the histogram plot of average risk index and analysis, the study reveals that larger diameter components are prone to turbulence which lead to greater risk of erosion. The risk of abnormal pressure drop and sanding up were considerably lower than that for erosion (abrasion).

The Genotype and Phenotype Features in a Large Chinese MFN2 Mutation Cohort.

Introduction: Charcot–Marie–Tooth disease type 2A (CMT2A) is a group of clinically and genetically heterogeneous disorders, which is mostly caused by mutations of the mitofusin2 (MFN2) gene. As the genotype–phenotype characteristics of CMT2A were still incompletely understood, we further explored the spectrum of CMT2A variants in China and demonstrated their phenotypic diversities.Methods: A total of 402 index patients/families with CMT throughout Mainland China were enrolled in this study. Among them, we analyzed 20 unrelated index cases with CMT2A by Sanger sequencing, next-generation sequencing, or whole-exome sequencing. Detailed clinical and genetic features of CMT2A patients were collected and analyzed. Of note, de novo mutations were not rare in MFN2 gene; we compared the clinical features of patients from the de novo group with those from the non-de novo group.Results: We identified 20 MFN2 variants, occupying 5.0% of CMT. Most patients presented with early onset and moderate phenotype with abnormal gait and foot drop as the main complaints at onset. Pyramidal signs accounts for 31.6% (6/19) in all patients, which is not uncommon. Four novel variants (p.Tyr752*, c.475-2A>G, p.Val99Met, and p.Arg275_Gln276insArg) were identified in the cohort. Besides, de novo variants occupied 35.0% (7/20) in our study with a much earlier age at onset compared with those in the non-de novo group (p = 0.021).Conclusion: Chinese CMT2A is a predominant typical pure CMT2A, with early onset and mild to moderate phenotype. Given the high frequency of de novo MFN2 mutations, genetic study should be considered for patients with early onset and severe idiopathic axonal neuropathy.

Long-Term Changes in the Land–Ocean Ecological Environment in Small Island Countries in the South Pacific: A Fiji Vision

Small island countries in the South Pacific are ecologically fragile areas, vulnerable to climate change, and the long-term ecological changes in the sea and land have an important impact on their sustainable development. This study takes Fiji, a typical small island country in the South Pacific, as an example, to analyze the change and connection of marine and terrestrial ecosystem environments based on 30 years of multi-source, satellite, remote-sensing data. From 1991 to 2019, according to the change in forest area in Fiji, three stages were delineated: first was a period of stability, then a decrease, and then a recovery in recent years. From 1991 to 2002, Fiji’s vegetation accounted for 73% of the total area; sea environment surrounding the islands, such as sea level height and sea surface temperature, were relatively low, with high water transparency. From 2002 to 2014, with the development of forestry and tourism, vegetation decreased by 6.89% and bare land increased, which changes the runoff erosion in the drainage basin; correspondingly, the chlorophyll a concentration in three major estuaries was found to be slightly increased with low water transparency. Meanwhile, coupled with the rising sea temperature, the area of Fiji’s coral reefs shrank significantly, with 51.13% of the total loss of coral reefs occurring in the Vanua Levu, where bare land and runoff were more distributed in its drainage basin. From 2014 to 2019, Fiji’s vegetation and coral reef areas recovered from the former stage; affected by short-term climate oscillations such as El Niño-Southern Oscillation (ENSO), the sea surface temperature showed a significant abnormal drop and the water transparency decreased. In the past 30 years (1993–2018), the sea level rise rate around Fiji reached 4 mm/year, and the temperature increased by 0.3 °C, which threatens the coastal ecosystem environment, including coral reefs and mangrove; inappropriate land-use change would worsen the situation in these ecologically fragile areas.

Digital twin for oil pipeline risk estimation using prognostic and machine learning techniques

Digital Twin technology is emerging as the digitization platform to enhance the industrial information processing and management in concern with virtual and physical entities. It paves the path for integrated industrial data analysis by combining IoT and Artificial Intelligence for better data interpretation. At present in oil industry, pipelines prevail to be feasible mode, the risk probability rate is getting increased and maintenance system becomes difficult with attention to the earlier prediction of accidents risks by undertaking entire pipeline. This paper aims to provide the frame structure of Digital Twin based on machine learning and prognostics algorithms model to analyze and predict the risk probability rate of oil pipeline system. Prognostics focuses on the detection of a failure precursor by estimating risk condition with respect to the pressure data towards the evaluation of remaining useful life (RUL). The abnormality of pressure attribute is taken in prognostic analysis for risk probability estimation followed by Dirichlet Process Clustering and Canopy clustering to segregate the abnormal pressure drop and rise. Using multiple oil substation data integration platform, the features are extracted using manifold learning methods and the best feature probability rates are evaluated using kernel based SVM algorithm to provide on-time control action on the entire oil pipeline system through efficient wireless data communication between server and the oil substations. As a result, the proposed work creates Virtual Intelligent Integrated Automated Control System to predict the risk rate in oil industry by integrating entire transmission lines through enhanced wireless information networks in remote locations.

Numerical analysis of stenoses severity and aortic wall mechanics in patients with supravalvular aortic stenosis

Supravalvular aortic stenosis (SVAS) is an aortic malformation characterized by a narrowing of the ascending aorta, resulting in abnormal hemodynamics and pressure drop across the stenosed region. It has been observed that the pressure drops measured from Doppler ultrasound exams often tend to be higher than those obtained from invasive cardiac catheterization. These misleadingly elevated pressure measurements may drive the decision to refer patients for surgical treatment prematurely.Considering this strong clinical association, the purpose of this work is to develop a computational modeling approach using a two-way coupled fluid-structure interaction methodology to determine an accurate prediction of trans-stenotic pressure drop and to further highlight the discrepancy between the SVAS assessment methods. Blood is modeled using Navier-Stokes equations while the aortic wall is simulated by a composite poroelastic structure to represent the three main layers of the arterial wall. The relationship between aortic wall elasticity and the blood flow conditions is examined in varying levels of stenosis, ranging from mild to severe degrees of vessel diameter narrowing.A substantial overestimation of the traditional Doppler pressure drop measurement is observed, especially for severe stenosis levels. The simulation results indicate that elasticity of the aortic wall has a relatively little effect on trans-stenotic pressure drop for the range of mild to moderate SVAS cases, but predicted to have a profound effect for severe SVAS cases. Moreover, significant sensitivity to the pressure drop across the SVAS region from stenosis severity is observed.

Modeling the Momentum Spillover Effect for Stock Prediction via Attribute-Driven Graph Attention Networks

In finance, the momentum spillovers of listed firms is well acknowledged. Only few studies predicted the trend of one firm in terms of its relevant firms. A common strategy of the pilot work is to adopt graph convolution networks (GCNs) with some predefined firm relations. However, momentum spillovers are propagated via a variety of firm relations, of which the bridging importance varies with time. Restricting to several predefined relations inevitably makes noise and thus misleads stock predictions. In addition, traditional GCNs transfer and aggregate the peer influences without considering the states of both connected firms once a connection is built. Such non-attribute sensibility makes traditional GCNs inappropriate to deal with the attribute-sensitive momentum spillovers of listed firms wherein the abnormal price drop of one firm may not spill over if the trade volume of this decreasing price is small or the prices of the linked firms are undervalued. In this study, we propose an attribute-driven graph attention network (AD-GAT) to address both problems in modeling momentum spillovers. This is achieved by element-wisely multiplying the nonlinear transformation of the attributes of the connected firms with the attributes of the source firm to consider its attribute-sensitive momentum spillovers, and applying the unmasked attention mechanism to infer the general dynamic firm relation from observed market signals fused by a novel tensor-based feature extractor. Experiments on the three-year data of the S&amp;P 500 demonstrate the superiority of the proposed framework over stateof-the-art algorithms, including GCN, eLSTM, and TGC.

Hot workability and dynamic recrystallization behavior of a spray formed 7055 aluminum alloy

In this work, the hot workability of a spray formed 7055 aluminum alloy was successfully investigated through advanced Gleeble thermo-mechanical simulation technology, and the EBSD technology was adopted to analyze the evolution of deformation microstructure comprehensively. From the results, the occurrence of the adiabatic heating phenomenon is confirmed, and an abnormal stress drop caused by which is then corrected by the extrapolation method. Moreover, it is noteworthy that with rising deformation parameters (including deformation temperature T from 350 °C to 450 °C, strain rate ε̇ from 1 s−1 to 20 s−1 and true strain ε from 0.1 to 0.8), the grains become finer, and the distributions of crystal orientation tend to be more homogeneous. Besides, it is found that both the Continuous Dynamic Recrystallization (CDRX) and Discontinuous Dynamic Recrystallization (DDRX) mechanisms coexist during hot deformation. Moreover, the increase of the three deformation parameters enlarges the misorientation angle inside the grains, which facilitates the DRX progress.

Research on Abnormal Output Current Drop of Solar Array of a Low Earth Orbit Satellite

This article describes the phenomenon of the abnormal output current drop of a solar array (SA) of a low Earth orbit satellite. All possible failure causes were analyzed in detail and the most likely reason was determined to be an open-circuit in solar cell interconnectors. The failure mechanisms were comprehensively analyzed from three aspects: solar cell replacement process, erosion of atomic oxygen, and thermal fatigue. The analysis results were verified by corresponding ground tests. For the on-orbit failure, a solution to slow down the output current drop of SA was given and verified to be effective by ground remote control operations on the satellite on orbit. The improvement measures for the development of SAs to avoid the output current drop are also proposed.