A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation

Nowadays, the rapid development of photovoltaic(PV) power stations requires increasingly reliable maintenance and fault diagnosis of PV modules in the field. Due to the effectiveness, convolutional neural network (CNN) has been widely used in the existing automatic defect detection of PV cells. However, the parameters of these CNN-based models are very large, which require stringent hardware resources and it is difficult to be applied in actual industrial projects. To solve these problems, we propose a novel lightweight high-performance model for automatic defect detection of PV cells in electroluminescence(EL) images based on neural architecture search and knowledge distillation. To auto-design an effective lightweight model, we introduce neural architecture search to the field of PV cell defect classification for the first time. Since the defect can be any size, we design a proper search structure of network to better exploit the multi-scale characteristic. To improve the overall performance of the searched lightweight model, we further transfer the knowledge learned by the existing pre-trained large-scale model based on knowledge distillation. Different kinds of knowledge are exploited and transferred, including attention information, feature information, logit information and task-oriented information. Experiments have demonstrated that the proposed model achieves the state-of-the-art performance on the public PV cell dataset of EL images under online data augmentation with accuracy of 91.74% and the parameters of 1.85M. The proposed lightweight high-performance model can be easily deployed to the end devices of the actual industrial projects and retain the accuracy.Comment: 12 pages, 7 figure

Spatio-Temporal Change of LakeWater Extent in Wuhan Urban Agglomeration Based on Landsat Images from 1987 to 2015

Urban lakes play an important role in urban development and environmental protection for the Wuhan urban agglomeration. Under the impacts of urbanization and climate change, understanding urban lake-water extent dynamics is significant. However, few studies on the lake-water extent changes for the Wuhan urban agglomeration exist. This research employed 1375 seasonally continuous Landsat TM/ETM+/OLI data scenes to evaluate the lake-water extent changes from 1987 to 2015. The random forest model was used to extract water bodies based on eleven feature variables, including six remote-sensing spectral bands and five spectral indices. An accuracy assessment yielded a mean classification accuracy of 93.11%, with a standard deviation of 2.26%. The calculated results revealed the following: (1) The average maximum lake-water area of the Wuhan urban agglomeration was 2262.17 km2 from 1987 to 2002, and it decreased to 2020.78 km2 from 2005 to 2015, with a loss of 241.39 km2 (10.67%). (2) The lake-water areas of loss of Wuhan, Huanggang, Xianning, and Xiaogan cities, were 114.83 km2, 44.40 km2, 45.39 km2, and 31.18 km2, respectively, with percentages of loss of 14.30%, 11.83%, 13.16%, and 23.05%, respectively. (3) The lake-water areas in the Wuhan urban agglomeration were 226.29 km2, 322.71 km2, 460.35 km2, 400.79 km2, 535.51 km2, and 635.42 km2 under water inundation frequencies of 5%–10%, 10%–20%, 20%–40%, 40%–60%, 60%–80%, and 80%–100%, respectively. The Wuhan urban agglomeration was approved as the pilot area for national comprehensive reform, for promoting resource-saving and environmentally friendly developments. This study could be used as guidance for lake protection and water resource management

A STUDY ON FOUR ANTIOXIDATION EFFECTS OF LYCIUM BARBARUM POLYSACCHARIDES IN VITRO

The objective of the study was to investigate the in vitro antioxidation activity of lycium barbarum polysaccharides (LBP). Ultraviolet spectrophotometry was adopted to determine the capability of LBP to clear superoxide anions, hydroxyl radicals, DPPH free radicals and ABTS free radicals. The result showed that the law for LBP to clear superoxide anions, hydroxyl radicals and DPPH free radicals was that the clearance rate increased gradually with the increase of the concentration, and when the concentration reached a certain value, the clearance rate leveled off, while the IC50 for clearing ABTS free radicals was 47.158±6.231μg/ml. The study concluded that LBP is a good in vitro antioxidant

Effectively control negative thermal expansion of single-phase ferroelectrics of PbTiO3-(Bi,La)FeO3 over a giant range

Control of negative thermal expansion is a fundamentally interesting topic in the negative thermal expansion materials in order for the future applications. However, it is a challenge to control the negative thermal expansion in individual pure materials over a large scale. Here, we report an effective way to control the coefficient of thermal expansion from a giant negative to a near zero thermal expansion by means of adjusting the spontaneous volume ferroelectrostriction (SVFS) in the system of PbTiO3-(Bi,La) FeO3 ferroelectrics. The adjustable range of thermal expansion contains most negative thermal expansion materials. The abnormal property of negative or zero thermal expansion previously observed in ferroelectrics is well understood according to the present new concept of spontaneous volume ferroelectrostriction. The present studies could be useful to control of thermal expansion of ferroelectrics, and could be extended to multiferroic materials whose properties of both ferroelectricity and magnetism are coupled with thermal expansion

MicroRNA-275 and its target vitellogenin-2 are crucial in ovary development and blood digestion of Haemaphysalis longicornis

Background: The hard tick Haemaphysalis longicornis is widely distributed in eastern Asia, New Zealand and Australia and is considered the major vector of Theileria and Babesia, harmful parasites to humans and animals. Female ticks need successful blood meals to complete the life-cycle. Therefore, elucidation of the underlying molecular mechanisms of H. longicornis development and reproduction is considered important for developing control strategies against the tick and tick-borne pathogens. Methods: Luciferase assays were used to identify the targets of micro RNA miR-275 in vitro. RNAi of Vitellogenin (Vg) was used in phenotype rescue experiments of ticks with miR-275 inhibition, and these analyses were used to identify the authentic target of miR-275 in vivo. The expression of miR-275 in different tissues and developmental stages of ticks was assessed by real-time PCR. To elucidate the functions of miR-275 in female ticks, we injected a miR-275 antagomir into female ticks and observed the phenotypic changes. Statistical analyses were performed with GraphPad5 using Student’s t-test. Results: In this study, we identified Vg-2 as an authentic target of miR-275 both in vitro and in vivo by luciferase assays and phenotype rescue experiments. miR-275 plays the regulatory role in a tissue-specific manner and differentially in developmental stages. Silencing of miR-275 resulted in blood digestion problems, substantially impaired ovary development and significantly reduced egg mass (P < 0.0001). Furthermore, RNAi silencing of Vg-2 not only impacted the blood meal uptake (P < 0.05) but also the egg mass (P < 0.05). Significant rescue was observed in miR-275 knockout ticks when RNAi was applied to Vg-2. Conclusion: To our knowledge, this study is the first demonstration that miR-275 targets Vg-2 in H. longicornis and regulates the functions of blood digestion and ovary development. These findings improve the molecular understanding of tick development and reproduction

Effect of Calcium Chloride on Energy Level and Quality of Tan Sheep Meat during Postmortem Aging

Our purpose was to clarify the effect of CaCl2 treatment on the energy level and quality of Tan sheep meat during postmortem aging. Tan sheep hind leg meat was injected with 200 mmol/L CaCl2 solution, and aged at 4 ℃ for 0, 2, 4, 6, and 8 days. The protein expression of phosphofructokinase (PFKM) was detected by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and Western blot to verify whether the glycolysis pathway could be activated by CaCl2. At the same time, the energy level and meat quality indexes were measured during postmortem aging to analyze the mechanism by which the glycolysis pathway affects the metabolic level and quality of Tan sheep meat. The results showed that the protein expression of PFKM decreased during postmortem aging, indicating activation of the glycolytic pathway. CaCl2 treatment accelerated the glycolysis process after slaughter, thereby promoting the decomposition of glycogen, increasing the production of lactic acid, and resulting in a rapid decrease in pH. Water-holding capacity (WHC), myofibril fragmentation index (MFI) and b* value were higher and shear force and a* were lower in the treatment group than in the control and blank groups. Therefore, CaCl2 could affect the color, WHC and tenderness of Tan sheep meat at different aging stages. By accelerating the decrease of pH and the accumulation of energy metabolites, the glycolytic pathway increased the level of energy metabolism during postmortem aging, while inhibiting the production of oxymyoglobin, activating the activity of calpain, changing the structure of myofibrils and shortening the space between muscle protein molecules, which accelerated the process of myofibril fragmentation and the hydrolysis of myofibrillar protein, and led to a deterioration in meat color and an increase in water loss and meat tenderness

Multi-view radiomics and deep learning modeling for prostate cancer detection based on multi-parametric MRI

IntroductionThis study aims to develop an imaging model based on multi-parametric MR images for distinguishing between prostate cancer (PCa) and prostate hyperplasia.MethodsA total of 236 subjects were enrolled and divided into training and test sets for model construction. Firstly, a multi-view radiomics modeling strategy was designed in which different combinations of radiomics feature categories (original, LoG, and wavelet) were compared to obtain the optimal input feature sets. Minimum-redundancy maximum-relevance (mRMR) selection and least absolute shrinkage selection operator (LASSO) were used for feature reduction, and the next logistic regression method was used for model construction. Then, a Swin Transformer architecture was designed and trained using transfer learning techniques to construct the deep learning models (DL). Finally, the constructed multi-view radiomics and DL models were combined and compared for model selection and nomogram construction. The prediction accuracy, consistency, and clinical benefit were comprehensively evaluated in the model comparison.ResultsThe optimal input feature set was found when LoG and wavelet features were combined, while 22 and 17 radiomic features in this set were selected to construct the ADC and T2 multi-view radiomic models, respectively. ADC and T2 DL models were built by transferring learning from a large number of natural images to a relatively small sample of prostate images. All individual and combined models showed good predictive accuracy, consistency, and clinical benefit. Compared with using only an ADC-based model, adding a T2-based model to the combined model would reduce the model’s predictive performance. The ADCCombinedScore model showed the best predictive performance among all and was transformed into a nomogram for better use in clinics.DiscussionThe constructed models in our study can be used as a predictor in differentiating PCa and BPH, thus helping clinicians make better clinical treatment decisions and reducing unnecessary prostate biopsies

Postmortem Degradation of Qinchuan Beef Protein by Proteasome and Its Mediated Ubiquitin-Proteasome Pathway

In this study, the Longissimus dorsi muscle of Qinchuan cattle was injected with the proteasome inhibitor MG-132 immediately postmortem and then stored at 4 ℃. The effect of the ubiquitin-proteasome pathway (UPP) on protein degradation as well as changes in the proteasome activity, ubiquitin content and microstructure of the muscle during postmortem storage was explored in order to provide theoretical support for precise postmortem regulation of beef quality. With the extension of storage time, proteasome activity was lower and the contents of total soluble protein and ubiquitin were higher in the MG-132 group than in the control group. The sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) results showed that the band intensity of total soluble proteins between 40 and 250 kDa was greater in the MG-132 group than in the control group; muscle structure was better preserved in the MG-132 group, and the Z line and the boundary between light and dark bands were clearer than those in the control group. The contents of total soluble protein and ubiquitin showed a significantly positive correlation (P < 0.05). In conclusion, postmortem injection of MG-132 inhibited the proteasome activity and the degradation of ubiquitinated proteins in the UPP in Qinchuan beef, which in turn altered protein degradation and attenuated muscle damage. This suggests that the UPP has a potential role in meat quality formation; the proteasome not only degrades proteins by itself alone to destroy beef myofibrillar structure, but also influences postmortem beef protein degradation through mediating the UPP, ultimately affecting postmortem beef quality

Evaluation of renal cold ischemia–reperfusion injury with intravoxel incoherent motion diffusion-weighted imaging and blood oxygenation level-dependent MRI in a rat model

Purpose: Cold ischemia-reperfusion injury (CIRI) is one of the most serious complications following renal transplantation. The current study investigated the feasibility of Intravoxel Incoherent Motion (IVIM) imaging and blood oxygenation level-dependent (BOLD) in the evaluation of different degrees of renal cold ischemia-reperfusion injury in a rat model.Methods: Seventy five rats were randomly divided into three groups (N = 25 for each group): T0: sham-operated group, T2/T4: CIRI groups with different cold ischemia hours (2, 4 h, respectively). The rat model of CIRI group was established by left kidney cold ischemia with right nephrectomy. All the rats received a baseline MRI before the surgery. Five rats in each group were randomly selected to undergo an MRI examination at 1 h, day 1, day 2 and day 5 after CIRI. The IVIM and BOLD parameters were studied in the renal cortex (CO), the outer stripe of the outer medulla (OSOM), and the inner stripe of the outer medulla (ISOM) followed by histological analysis to examine Paller scores, peritubular capillary (PTC) density, apoptosis rate and biochemical indicators to obtain the contents of serum creatinine (Scr), blood urea nitrogen (BUN), superoxide dismutase (SOD) and malondialdehyde (MDA).Results: The D, D*, PF and T2* values in the CIRI groups were lower than those in the sham-operated group at all timepoints (all p &lt; 0.05). The prolonged cold ischemia times resulted in gradually lower D, D*, PF and T2* values (all p &lt; 0.05). The D and T2* values of cortex and OSOM in Group T0 and T2 returned to the baseline level (all p &gt; 0.05) except Group T4. The D* and PF values of cortex, OSOM and ISOM in Group T2 and T4 still remained below the normal levels (all p &lt; 0.05) except Group T0. D, D*, PF and T2* values were strongly correlated with histopathological (Paller scores, PTC density and apoptosis rate) and the biochemistry indicators (SOD and MDA) (|r|&gt;0.6, p &lt; 0.001). D*, PF and T2* values were moderately to poorly correlated with some biochemistry indicators (Scr and BUN) (|r|&lt;0.5, p &lt; 0.05).Conclusion: IVIM and BOLD can serve as noninvasive radiologic markers for monitoring different degrees of renal impairment and recovery after renal CIRI

The predictive model for COVID‑19 pandemic plastic pollution by using deep learning method

Pandemic plastics (e.g., masks, gloves, aprons, and sanitizer bottles) are global consequences of COVID-19 pandemic-infected waste, which has increased significantly throughout the world. These hazardous wastes play an important role in environmental pollution and indirectly spread COVID-19. Predicting the environmental impacts of these wastes can be used to provide situational management, conduct control procedures, and reduce the COVID-19 effects. In this regard, the presented study attempted to provide a deep learning-based predictive model for forecasting the expansion of the pandemic plastic in the megacities of Iran. As a methodology, a database was gathered from February 27, 2020, to October 10, 2021, for COVID-19 spread and personal protective equipment usage in this period. The dataset was trained and validated using training (80%) and testing (20%) datasets by a deep neural network (DNN) procedure to forecast pandemic plastic pollution. Performance of the DNN-based model is controlled by the confusion matrix, receiver operating characteristic (ROC) curve, and justified by the k-nearest neighbours, decision tree, random forests, support vector machines, Gaussian naïve Bayes, logistic regression, and multilayer perceptron methods. According to the comparative modelling results, the DNN-based model was found to predict more accurately than other methods and have a significant predominance over others with a lower errors rate (MSE = 0.024, RMSE = 0.027, MAPE = 0.025). The ROC curve analysis results (overall accuracy) indicate the DNN model (AUC = 0.929) had the highest score among others